Method and device for predicting physiological values

ABSTRACT

The invention relates generally to methods, systems, and devices for measuring the concentration of target analytes present in a biological system using a series of measurements obtained from a monitoring system and a Mixtures of Experts (MOE) algorithm. In one embodiment, the present invention describes a method for measuring blood glucose in a subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation-in-part of U.S. patentapplication Ser. No. 09/241,929, filed Feb. 1, 1999, which is acontinuation-in-part of U.S. patent application Ser. No. 09/198,039,filed Nov. 23, 1998, which is a continuation-in-part of U.S. patentapplication Ser. No. 09/163,856, filed Sep. 30, 1998, all applicationsare herein incorporated by reference in their entireties.

FIELD OF THE INVENTION

[0002] The invention relates generally to a method and device formeasuring the concentration of target chemical analytes present in abiological system. More particularly, the invention relates to a methodand monitoring systems for predicting a concentration of an analyteusing a series of measurements obtained from a monitoring system and aMixtures of Experts (MOE) algorithm.

BACKGROUND OF THE INVENTION

[0003] The Mixtures of Experts model is a statistical method forclassification and regression (Waterhouse, S., “Classification andRegression Using Mixtures of Experts, October 1997, Ph. D. Thesis,Cambridge University). Waterhouse discusses Mixtures of Experts modelsfrom a theoretical perspective and compares them with other models, suchas, trees, switching regression models, modular networks. The firstextension described in Waterhouse's thesis is a constructive algorithmfor learning model architecture and parameters, which is inspired byrecursive partitioning. The second extension described in Waterhouse'sthesis uses Bayesian methods for learning the parameters of the model.These extensions are compared empirically with the standard Mixtures ofExperts model and with other statistical models on small to medium sizeddata sets. Waterhouse also describes the application of the Mixtures ofExperts framework to acoustic modeling within a large vocabulary speechrecognition system.

[0004] The Mixtures of Experts model has been employed in proteinsecondary structure prediction (Barlow, T. W., Journal Of MolecularGraphics, 13(3), p. 175-183, 1995). In this method input data wereclustered and used to train a series different networks. Application ofa Hierarchical Mixtures of Experts to the prediction of proteinsecondary structure was shown to provide no advantages over a singlenetwork.

[0005] Mixtures of Experts algorithms have also been applied to theanalysis of a variety of different kinds of data sets including thefollowing: human motor systems (Ghahramani, Z. and Wolpert, D. M.,Nature, 386(6623):392-395, 1997); and economic analysis (Hamilton, J. D.and Susmel, R., Journal of Econometrics, 64(1-2):307-333, 1994).

SUMMARY OF THE INVENTION

[0006] The present invention provides a method and device (for example,a monitoring or sampling system) for continually or continuouslymeasuring the concentration of an analyte present in a biologicalsystem. The method entails continually or continuously detecting a rawsignal from the biological system, wherein the raw signal isspecifically related to the analyte. A calibration step is performed tocorrelate the raw signal with a measurement value indicative of theconcentration of analyte present in the biological system. These stepsof detection and calibration are used to obtain a series of measurementvalues at selected time intervals. Once the series of measurement valuesis obtained, the method of the invention provides for the prediction ofa measurement value using a Mixtures of Experts (MOE) algorithm.

[0007] The raw signal can be obtained using any suitable sensingmethodology including, for example, methods which rely on direct contactof a sensing apparatus with the biological system; methods which extractsamples from the biological system by invasive, minimally invasive, andnon-invasive sampling techniques, wherein the sensing apparatus iscontacted with the extracted sample; methods which rely on indirectcontact of a sensing apparatus with the biological system; and the like.In preferred embodiments of the invention, methods are used to extractsamples from the biological sample using minimally invasive ornon-invasive sampling techniques. The sensing apparatus used with any ofthe above-noted methods can employ any suitable sensing element toprovide the raw signal including, but not limited to, physical,chemical, electrochemical, photochemical, spectrophotometric,polarimetric, calorimetric, radiometric, or like elements. In preferredembodiments of the invention, a biosensor is used which comprises anelectrochemical sensing element.

[0008] In one particular embodiment of the invention, the raw signal isobtained using a transdermal sampling system that is placed in operativecontact with a skin or mucosal surface of the biological system. Thesampling system transdermally extracts the analyte from the biologicalsystem using any appropriate sampling technique, for example,iontophoresis. The transdermal sampling system is maintained inoperative contact with the skin or mucosal surface of the biologicalsystem to provide for continual or continuous analyte measurement.

[0009] In a preferred embodiment of the invention, a Mixtures of Expertsalgorithm is used to predict measurement values. The general Mixtures ofExperts algorithm is represented by the following series of equations:where the individual experts have a linear form: $\begin{matrix}{{An} = {\sum\limits_{i = 1}^{n}{{An}_{i}w_{i}}}} & (1)\end{matrix}$

[0010] wherein (An) is an analyte of interest, n is the number ofexperts, An_(i) is the analyte predicted by Expert i; and w_(i) is aparameter, and the individual experts An_(i) are further defined by theexpression shown as Equation (2) $\begin{matrix}{{An}_{i} = {{\sum\limits_{j = 1}^{n}{a_{ij}P_{j}}} + z_{i}}} & (2)\end{matrix}$

[0011] wherein, An_(i) is the analyte predicted by Expert i; P_(j) isone of m parameters, m is typically less than 100; a_(ij) arecoefficients; and z_(i) is a constant; and further where the weightingvalue, w_(i), is defined by the formula shown as Equation (3).$\begin{matrix}{w_{i} = \frac{^{d_{i}}}{\left\lbrack {\sum\limits_{k = 1}^{n}^{d_{k}}} \right\rbrack}} & (3)\end{matrix}$

[0012] where e refers to the exponential function and the d_(k) (notethat the d_(i) in the numerator of Equation 3 is one of the d_(k)) are aparameter set analogous to Equation 2 that is used to determine theweights w_(i). The d_(k) are given by Equation 4. $\begin{matrix}{d_{k} = {{\sum\limits_{j = 1}^{m}{\alpha_{jk}P_{j}}} + \omega_{k}}} & (4)\end{matrix}$

[0013] where α_(jk) is a coefficient, P_(j) is one of m parameters, andwhere ω_(k) is a constant.

[0014] Another object of the invention to use the Mixtures of Expertsalgorithm of the invention to predict blood glucose values. In oneaspect, the method of the invention is used in conjunction with aniontophoretic sampling device that provides continual or continuousblood glucose measurements. In one embodiment the Mixtures of Expertsalgorithm is essentially as follows: where the individual experts have alinear form

BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (5)

[0015] wherein (BG) is blood glucose, there are three experts (n=3) andBG_(i) is the analyte predicted by Expert i; w_(i) is a parameter, andthe individual Experts BG_(i) are further defined by the expressionshown as Equations 6, 7, and 8

BG ₁ =p ₁(time)+q ₁(active)+r ₁(signal)+s ₁(BG|cp)+t ₁   (6)

[0016]BG ₂ =p ₂(time)+q ₂(active)+r ₂(signal)+s ₂(BG|cp)+t ₂   (7)

BG ₃ =p ₃(time)+q ₃(active)+r ₃(signal)+s ₃(BG|cp)+t ₃   (8)

[0017] wherein, BG_(i) is the analyte predicted by Expert i; parametersinclude, time (elapsed time since the sampling system was placed inoperative contact with said biological system), active (active signal),signal (calibrated signal), and BG/cp (blood glucose value at acalibration point); p_(i), q_(i), r_(i), and s_(i) are coefficients; andt_(i) is a constant; and further where the weighting value, w_(i), isdefined by the formulas shown as Equations 9, 10, and 11 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (9) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (10) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (11)\end{matrix}$

[0018] where e refers to the exponential function and d_(i) is aparameter set (analogous to Equations 6, 7, and 8) that are used todetermine the weights w_(i), given by Equations 9, 10, and 11, and

d ₁=τ₁(time)+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (12)

d ₂=τ₂(time)+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (13)

d ₃=τ₃(time)+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (14)

[0019] where τ_(i), β_(i), γ_(i) and δ_(i) are coefficients, and where∈_(i) is a constant.

[0020] In another embodiment for the prediction of blood glucose values,the Mixtures of Experts algorithm is essentially as follows: where theindividual experts have a linear form

BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (15)

[0021] wherein (BG) is blood glucose, there are three experts (n=3) andBG_(i) is the analyte predicted by Expert i; w_(i) is a parameter, andthe individual Experts BG_(i) are further defined by the expressionshown as Equations 16, 17, and 18

BG ₁ =p ₁(time_(c))+q ₁(active)+r ₁(signal)+s ₁(BG|cp)+t ₁   (16)

BG ₂ =p ₂(time_(c))+q ₂(active)+r ₂(signal)+s ₂(BG|cp)+t ₂   (17)

BG ₃ =p ₃(time_(c))+q ₃(active)+r ₃(signal)+s ₃(BG|cp)+t ₃   (16)

[0022] wherein, BG_(i) is the analyte predicted by Expert i; parametersinclude, time_(c) (elapsed time since calibration of said samplingsystem), active (active signal), signal (calibrated signal), and BG/cp(blood glucose value at a calibration point); p_(i), q_(i), r_(i), ands_(i) are coefficients; and t_(i) is a constant; and further where theweighting value, w_(i), is defined by the formulas shown as Equations19, 20, and 21 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (19) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (20) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (21)\end{matrix}$

[0023] where e refers to the exponential function and d_(i) is aparameter set (analogous to Equations 6, 7, and 8) that are used todetermine the weights w_(i), given by Equations 19, 20, and 21, and

d ₁ =τ ₁(time_(c))+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (22)

d ₂ =τ ₂(time_(c))+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (23)

d ₃ =τ ₃(time_(c))+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (24)

[0024] where τ_(i), β_(i), γ_(i) and δ_(i) are coefficients, and where∈_(i) is a constant.

[0025] Parameters can be substituted, and/or other parameters can beincluded in these calculations, for example, time parameters can bevaried (e.g., as described above, elapsed time since the sampling systemwas placed in contact with a biological system, or elapsed time sincethe sampling system was calibrated) or multiple time parameters can beused in the same equation where these parameters are appropriatelyweighted. Further parameters include, but are not limited to,temperature, ionophoretic voltage, and skin conductivity. In addition, acalibration check can be used to insure an efficacious calibration.

[0026] A further object of the invention to provide a method formeasuring an analyte, for example, blood glucose, in a subject. In oneembodiment, the method entails operatively contacting a glucose sensingapparatus with the subject to detect blood glucose and thus obtain a rawsignal from the sensing apparatus. The raw signal is specificallyrelated to the glucose, and is converted into a measurement valueindicative of the subject's blood glucose concentration using acalibration step. In one aspect of the invention, the sensing apparatusis a near-IR spectrometer. In another aspect of the invention, thesensing means comprises a biosensor having an electrochemical sensingelement.

[0027] It is also an object of the invention to provide a monitoringsystem for continually or continuously measuring an analyte present in abiological system. The monitoring system is formed from the operativecombination of a sampling means, a sensing means, and a microprocessormeans which controls the sampling means and the sensing means. Thesampling means is used to continually or continuously extract theanalyte from the biological system across a skin or mucosal surface ofsaid biological system. The sensing means is arranged in operativecontact with the analyte extracted by the sampling means, such that thesensing means can obtain a raw signal from the extracted analyte whichsignal is specifically related to the analyte. The microprocessor meanscommunicates with the sampling means and the sensing means, and is usedto: (a) control the sampling means and the sensing means to obtain aseries of raw signals at selected time intervals during a continual orcontinuous measurement period; (b) correlate the raw signals withmeasurement values indicative of the concentration of analyte present inthe biological system; and (c) predict a measurement value using theMixtures of Experts algorithm. In one aspect, the monitoring system usesan iontophoretic current to extract the analyte from the biologicalsystem.

[0028] It is a further object of the invention to provide a monitoringsystem for measuring blood glucose in a subject. The monitoring systemis formed from an operative combination of a sensing means and amicroprocessor means. The sensing means is adapted for operative contactwith the subject or with a glucose-containing sample extracted from thesubject, and is used to obtain a raw signal specifically related toblood glucose in the subject. The microprocessor means communicates withthe sensing means, and is used to: (a) control the sensing means toobtain a series of raw signals (specifically related to blood glucose)at selected time intervals; (b) correlate the raw signals withmeasurement values indicative of the concentration of blood glucosepresent in the subject; and (c) predict a measurement value using theMixtures of Experts algorithm.

[0029] In a further aspect, the monitoring system comprises a biosensorhaving an electrochemical sensing element. In another aspect, themonitoring system comprises a near-IR spectrometer.

[0030] Additional objects, advantages and novel features of theinvention will be set forth in part in the description which follows,and in part will become apparent to those skilled in the art uponexamination of the following, or may be learned by practice of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0031]FIG. 1A depicts a top plan view of an iontophoretic collectionreservoir and electrode assembly for use in a transdermal samplingdevice constructed according to the present invention.

[0032]FIG. 1B depicts the side view of the iontophoretic collectionreservoir and electrode assembly shown in FIG. 1A.

[0033]FIG. 2 is a pictorial representation of an iontophoretic samplingdevice which includes the iontophoretic collection reservoir andelectrode assembly of FIGS. 1A and 1B.

[0034]FIG. 3 is an exploded pictorial representation of components froma preferred embodiment of the automatic sampling system of the presentinvention.

[0035]FIG. 4 is a representation of one embodiment of a bimodalelectrode design. The figure presents an overhead and schematic view ofthe electrode assembly 433. In the figure, the bimodal electrode isshown at 430 and can be, for example, a Ag/AgCl iontophoretic/counterelectrode. The sensing or working electrode (made from, for example,platinum) is shown at 431. The reference electrode is shown at 432 andcan be, for example, a Ag/AgCl electrode. The components are mounted ona suitable nonconductive substrate 434, for example, plastic or ceramic.The conductive leads 437 (represented by dotted lines) leading to theconnection pad 435 are covered by a second nonconductive piece 436 (thearea represented by vertical striping) of similar or different material(e.g., plastic or ceramic). In this example of such an electrode theworking electrode area is approximately 1.35 cm². The dashed line inFIG. 4 represents the plane of the cross-sectional schematic viewpresented in FIG. 5.

[0036]FIG. 5 is a representation of a cross-sectional schematic view ofthe bimodal electrodes as they may be used in conjunction with areference electrode and a hydrogel pad. In the figure, the componentsare as follows: bimodal electrodes 540 and 541; sensing electrodes 542and 543; reference electrodes 544 and 545; a substrate 546; and hydrogelpads 547 and 548.

[0037]FIG. 6 depicts predicted blood glucose data (using the Mixtures ofExperts algorithm) versus measured blood glucose data, as described inExample 2.

[0038]FIG. 7 depicts predicted blood glucose data (using the Mixtures ofExperts algorithm) versus measured blood glucose data, as described inExample 4.

[0039]FIG. 8 presents a graph of the measured and predicted bloodglucose levels vs. time, as described in Example 4.

[0040]FIG. 9 depicts an exploded view of an embodiment of an autosensor.

[0041]FIGS. 10A and 10B graphically illustrate the method of the presentinvention used for decreasing the bias of a data set.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0042] Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular compositionsor biological systems, as such may vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting.

[0043] It must be noted that, as used in this specification and theappended claims, the singular forms “a”, “an” and “the” include pluralreferents unless the content clearly dictates otherwise. Thus, forexample, reference to “an analyte” includes mixtures of analytes, andthe like.

[0044] All publications, patents and patent applications cited herein,whether supra or infra, are hereby incorporated by reference in theirentirety.

[0045] Unless defined otherwise, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although any methodsand materials similar or equivalent to those described herein can beused in the practice for testing of the present invention, the preferredmaterials and methods are described herein.

[0046] In describing and claiming the present invention, the followingterminology will be used in accordance with the definitions set outbelow.

[0047] 1.0.0 Definitions

[0048] The terms “analyte” and “target analyte” are used herein todenote any physiological analyte of interest that is a specificsubstance or component that is being detected and/or measured in achemical, physical, enzymatic, or optical analysis. A detectable signal(e.g., a chemical signal or electrochemical signal) can be obtained,either directly or indirectly, from such an analyte or derivativesthereof. Furthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

[0049] A “sampling device” or “sampling system” refers to any device forobtaining a sample from a biological system for the purpose ofdetermining the concentration of an analyte of interest. As used herein,the term “sampling” means invasive, minimally invasive or non-invasiveextraction of a substance from the biological system, generally across amembrane such as skin or mucosa. The membrane can be natural orartificial, and can be of plant or animal nature, such as natural orartificial skin, blood vessel tissue, intestinal tissue, and the like.Typically, the sampling means are in operative contact with a“reservoir,” or “collection reservoir,” wherein the sampling means isused for extracting the analyte from the biological system into thereservoir to obtain the analyte in the reservoir. A “biological system”includes both living and artificially maintained systems. Examples ofminimally invasive and noninvasive sampling techniques includeiontophoresis, sonophoresis, suction, electroporation, thermal poration,passive diffusion, microfine (miniature) lances or cannulas,subcutaneous implants or insertions, and laser devices. Sonophoresisuses ultrasound to increase the permeability of the skin (see, e.g.,Menon et al. (1994) Skin Pharmacology 7:130-139). Suitable sonophoresissampling systems are described in International Publication No. WO91/12772, published Sep. 5, 1991. Passive diffusion sampling devices aredescribed, for example, in International Publication Nos.: WO 97/38126(published Oct. 16, 1997); WO 97/42888, WO 97/42886, WO 97/42885, and WO97/42882 (all published Nov. 20, 1997); and WO 97/43962 (published Nov.27, 1997). Laser devices use a small laser beam to burn a hole throughthe upper layer of the patient's skin (see, e.g., Jacques et al. (1978)J. Invest. Dermatology 88:88-93). Examples of invasive samplingtechniques include traditional needle and syringe or vacuum sample tubedevices.

[0050] The term “collection reservoir” is used to describe any suitablecontainment means for containing a sample extracted from a biologicalsystem. For example, the collection reservoir can be a receptaclecontaining a material which is ionically conductive (e.g., water withions therein), or alternatively, it can be a material, such as, asponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the form of a disk or pad). Hydrogels are typically referredto as “collection inserts.” Other suitable collection reservoirsinclude, but are not limited to, tubes, vials, capillary collectiondevices, cannulas, and miniaturized etched, ablated or molded flowpaths.

[0051] A “housing” for the sampling system can further include suitableelectronics (e.g., microprocessor, memory, display and other circuitcomponents) and power sources for operating the sampling system in anautomatic fashion.

[0052] A “monitoring system,” as used herein, refers to a system usefulfor continually or continuously measuring a physiological analytepresent in a biological system. Such a system typically includes, but isnot limited to, sampling means, sensing means, and a microprocessormeans in operative communication with the sampling means and the sensingmeans.

[0053] The term “artificial,” as used herein, refers to an aggregationof cells of monolayer thickness or greater which are grown or culturedin vivo or in vitro, and which function as a tissue of an organism butare not actually derived, or excised, from a pre-existing source orhost.

[0054] The term “subject” encompasses any warm-blooded animal,particularly including a member of the class Mammalia such as, withoutlimitation, humans and nonhuman primates such as chimpanzees and otherapes and monkey species; farm animals such as cattle, sheep, pigs, goatsand horses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex. Thus, adult and newbornsubjects, as well as fetuses, whether male or female, are intended to becovered.

[0055] As used herein, the term “continual measurement” intends a seriesof two or more measurements obtained from a particular biologicalsystem, which measurements are obtained using a single device maintainedin operative contact with the biological system over the time period inwhich the series of measurements is obtained. The term thus includescontinuous measurements.

[0056] The term “transdermal,” as used herein, includes both transdermaland transmucosal techniques, i.e., extraction of a target analyte acrossskin or mucosal tissue. Aspects of the invention which are describedherein in the context of “transdermal,” unless otherwise specified, aremeant to apply to both transdermal and transmucosal techniques.

[0057] The term “transdermal extraction,” or “transdermally extracted”intends any noninvasive, or at least minimally invasive sampling method,which entails extracting and/or transporting an analyte from beneath atissue surface across skin or mucosal tissue. The term thus includesextraction of an analyte using iontophoresis (reverse iontophoresis),electroosmosis, sonophoresis, microdialysis, suction, and passivediffusion. These methods can, of course, be coupled with application ofskin penetration enhancers or skin permeability enhancing technique suchas tape stripping or pricking with micro-needles. The term“transdermally extracted” also encompasses extraction techniques whichemploy thermal poration, electroporation, microfine lances, microfinecanulas, subcutaneous implants or insertions, and the like.

[0058] The term “iontophoresis” intends a method for transportingsubstances across tissue by way of an application of electrical energyto the tissue. In conventional iontophoresis, a reservoir is provided atthe tissue surface to serve as a container of material to betransported. Iontophoresis can be carried out using standard methodsknown to those of skill in the art, for example, by establishing anelectrical potential using a direct current (DC) between fixed anode andcathode “iontophoretic electrodes,” alternating a direct current betweenanode and cathode iontophoretic electrodes, or using a more complexwaveform such as applying a current with alternating polarity (AP)between iontophoretic electrodes (so that each electrode is alternatelyan anode or a cathode).

[0059] The term “reverse iontophoresis” refers to the movement of asubstance from a biological fluid across a membrane by way of an appliedelectric potential or current. In reverse iontophoresis, a reservoir isprovided at the tissue surface to receive the extracted material.

[0060] “Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

[0061] The term “sensing device,” “sensing means,” or “biosensor device”encompasses any device that can be used to measure the concentration ofan analyte, or derivative thereof, of interest. Preferred sensingdevices for detecting blood analytes generally include electrochemicaldevices and chemical devices. Examples of electrochemical devicesinclude the Clark electrode system (see, e.g., Updike, et al., (1967)Nature 214:986-988), and other amperometric, coulometric, orpotentiometric electrochemical devices. Examples of chemical devicesinclude conventional enzyme-based reactions as used in the Lifescan®glucose monitor (Johnson and Johnson, New Brunswick, N.J.) (see, e.g.,U.S. Pat. No. 4,935,346 to Phillips, et al.).

[0062] A “biosensor” or “biosensor device” includes, but is not limitedto, a “sensor element” which includes, but is not limited to, a“biosensor electrode” or “sensing electrode” or “working electrode”which refers to the electrode that is monitored to determine the amountof electrical signal at a point in time or over a given time period,which signal is then correlated with the concentration of a chemicalcompound. The sensing electrode comprises a reactive surface whichconverts the analyte, or a derivative thereof, to electrical signal. Thereactive surface can be comprised of any electrically conductivematerial such as, but not limited to, platinum-group metals (including,platinum, palladium, rhodium, ruthenium, osmium, and iridium), nickel,copper, silver, and carbon, as well as, oxides, dioxides, combinationsor alloys thereof. Some catalytic materials, membranes, and fabricationtechnologies suitable for the construction of amperometric biosensorswere described by Newman, J. D., et al. (Analytical Chemistry 67 (24),4594-4599, 1995).

[0063] The “sensor element” can include components in addition to abiosensor electrode, for example, it can include a “referenceelectrode,” and a “counter electrode.” The term “reference electrode” isused herein to mean an electrode that provides a reference potential,e.g., a potential can be established between a reference electrode and aworking electrode. The term “counter electrode” is used herein to meanan electrode in an electrochemical circuit which acts as a currentsource or sink to complete the electrochemical circuit. Although it isnot essential that a counter electrode be employed where a referenceelectrode is included in the circuit and the electrode is capable ofperforming the function of a counter electrode, it is preferred to haveseparate counter and reference electrodes because the referencepotential provided by the reference electrode is most stable when it isat equilibrium. If the reference electrode is required to act further asa counter electrode, the current flowing through the reference electrodemay disturb this equilibrium. consequently, separate electrodesfunctioning as counter and reference electrodes are most preferred.

[0064] In one embodiment, the “counter electrode” of the “sensorelement” comprises a “bimodal electrode.” The term “bimodal electrode”as used herein typically refers to an electrode which is capable offunctioning non-simultaneously as, for example, both the counterelectrode (of the “sensor element”) and the iontophoretic electrode (ofthe “sampling means”).

[0065] The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an electrolyte containingmaterial (e.g. gel) which contains an analyte or through which ananalyte, or a derivative thereof, flows from a source thereof; (2) iscomprised of a catalytic material (e.g., carbon, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (e.g. hydrogen peroxide) intoan electrical signal (e.g., an electrical current); and (4) defines theelectrode surface area that, when composed of a reactive material, issufficient to drive the electrochemical reaction at a rate sufficient togenerate a detectable, reproducibly measurable, electrical signal thatis correlatable with the amount of analyte present in the electrolyte.

[0066] An “ionically conductive material” refers to any material thatprovides ionic conductivity, and through which electrochemically activespecies can diffuse. The ionically conductive material can be, forexample, a solid, liquid, or semi-solid (e.g., in the form of a gel)material that contains an electrolyte, which can be composed primarilyof water and ions (e.g., sodium chloride), and generally comprises 50%or more water by weight. The material can be in the form of a gel, asponge or pad (e.g., soaked with an electrolytic solution), or any othermaterial that can contain an electrolyte and allow passage therethroughof electrochemically active species, especially the analyte of interest.

[0067] The term “physiological effect” encompasses effects produced inthe subject that achieve the intended purpose of a therapy. In preferredembodiments, a physiological effect means that the symptoms of thesubject being treated are prevented or alleviated. For example, aphysiological effect would be one that results in the prolongation ofsurvival in a patient.

[0068] A “laminate” , as used herein, refers to structures comprised ofat least two bonded layers. The layers may be bonded by welding orthrough the use of adhesives. Examples of welding include, but are notlimited to, the following: ultrasonic welding, heat bonding, andinductively coupled localized heating followed by localized flow.Examples of common adhesives include, but are not limited to, pressuresensitive adhesives, thermoset adhesives, cyanocrylate adhesives,epoxies, contact adhesives, and heat sensitive adhesives.

[0069] A “collection assembly”, as used herein, refers to structurescomprised of several layers, where the assembly includes at least onecollection insert, for example a hydrogel. An example of a collectionassembly of the present invention is a mask layer, collection inserts,and a retaining layer where the layers are held in appropriate,functional relationship to each other but are not necessarily alaminate, i.e., the layers may not be bonded together. The layers may,for example, be held together by interlocking geometry or friction.

[0070] An “autosensor assembly”, as used herein, refers to structuresgenerally comprising a mask layer, collection inserts, a retaininglayer, an electrode assembly, and a support tray. The autosensorassembly may also include liners. The layers of the assembly are held inappropriate, functional relationship to each other.

[0071] The mask and retaining layers are preferably composed ofmaterials that are substantially impermeable to the analyte (chemicalsignal) to be detected (e.g., glucose); however, the material can bepermeable to other substances. By “substantially impermeable” is meantthat the material reduces or eliminates chemical signal transport (e.g.,by diffusion). The material can allow for a low level of chemical signaltransport, with the proviso that chemical signal that passes through thematerial does not cause significant edge effects at the sensingelectrode.

[0072] “Substantially planar” as used herein, includes a planar surfacethat contacts a slightly curved surface, for example, a forearm or upperarm of a subject. A “substantially planar” surface is, for example, asurface having a shape to which skin can conform, i.e., contactingcontact between the skin and the surface.

[0073] A “Mixtures of Experts (MOE)” algorithm is used in the practiceof the present invention. An example of a Mixtures of Experts algorithmuseful in connection with the present invention is represented by thefollowing equations, where the individual experts have a linear form:$\begin{matrix}{{An} = {\sum\limits_{i = 1}^{n}{{An}_{i}w_{i}}}} & (1)\end{matrix}$

[0074] wherein (An) is an analyte of interest, n is the number ofexperts, An_(i) is the analyte predicted by Expert i; and w_(i) is aparameter, and the individual experts An_(i) are further defined by theexpression shown as Equation (2) $\begin{matrix}{{An}_{i} = {{\sum\limits_{j = 1}^{n}{a_{ij}P_{j}}} + z_{i}}} & (2)\end{matrix}$

[0075] wherein, An_(i) is the analyte predicted by Expert i; P_(j) isone of m parameters, m is typically less than 100; a_(ij) arecoefficients; and z_(i) is a constant; and further where the weightingvalue, w_(i), is defined by the formula shown as Equation (3).$\begin{matrix}{w_{i} = \frac{^{d_{i}}}{\left\lbrack {\sum\limits_{k = 1}^{n}^{d_{k}}} \right\rbrack}} & (3)\end{matrix}$

[0076] where e refers to the exponential function the d_(k) (note thatthe d_(i) in the numerator of Equation 3 is one of the d_(k)) are aparameter set analogous to Equation 2 that is used to determine theweights w_(i). The d_(k) are given by Equation 4. $\begin{matrix}{d_{k} = {{\sum\limits_{j = 1}^{m}{\alpha_{jk}P_{j}}} + \omega_{k}}} & (4)\end{matrix}$

[0077] where α_(jk) is a coefficient, P_(j) is one of m parameters, andwhere ω_(k) is a constant.

[0078] The Mixtures of Experts algorithm is a generalized predictivetechnology for data analysis. This method uses a superposition ofmultiple linear regressions, along with a switching algorithm, topredict outcomes. Any number of input/output variables are possible. Theunknown coefficients in this method are determined by a maximumposterior probability technique.

[0079] The method is typically implemented as follows. An experimentaldata set of input/output pairs is assembled that spans the expectedranges of all variables. These variables are then used to train theMixtures of Experts (that is, used to determine the unknowncoefficients). These coefficients are determined using, for example, theExpectation Maximization method (Dempster, A. P., N. M. Laird, and D. B.Rubin, J. Royal Statistical Society (Series B-Methodological) 39:(1),1977). Once these coefficients are known, the Mixtures of Experts iseasily applied to a new data set.

[0080] “Parameter” as used herein refers to an arbitrary constant orvariable so appearing in a mathematical expression that changing it givevarious cases of the phenomenon represented (McGraw-Hill Dictionary ofScientific and Technical Terms, S. P. Parker, ed., Fifth Edition,McGraw-Hill Inc., 1994). In the context of the GlucoWatch® monitor(Cygnus, Inc., Redwood City, Calif.), a parameter is a variable thatinfluences the value of the blood glucose level as calculated by analgorithm. For the Mixtures of Experts algorithm, these parametersinclude, but are not limited to, the following: time (e.g., elapsed timesince the monitor was applied to a subject; and/or elapsed time sincecalibration); the active signal; the calibrated signal; the bloodglucose value at the calibration point; the skin temperature; the skinconductivity; and the iontophoretic voltage. Changes in the values ofany of these parameters can be expected to change the value of thecalculated blood glucose value. Parameters can be substituted, and/orother parameters can be included in these calculations, for example,time parameters can be varied (e.g., elapsed time since the samplingsystem was placed in contact with a biological system, or elapsed timesince the sampling system was calibrated) or multiple time parameterscan be used in the same equation where these parameters areappropriately weighted.

[0081] By the term “printed” as used herein is meant a substantiallyuniform deposition of an electrode formulation onto one surface of asubstrate (i.e., the base support). It will be appreciated by thoseskilled in the art that a variety of techniques may be used to effectsubstantially uniform deposition of a material onto a substrate, e.g.,Gravure-type printing, extrusion coating, screen coating, spraying,painting, or the like.

[0082] “Bias” as used herein refers to the difference between theexpected value of an estimator and the true value of a parameter. “Bias”is used in a statistical context, in particular, in estimating the valueof a parameter of a probability distribution. For example, in the caseof a linear regression wherein

[0083] y=mx+b, for x=a,

[0084] the bias at “a” equals (ma+b)−a.

[0085] “Decay” as used herein refers to a gradual reduction in themagnitude of a quantity, for example, a current detected using a sensorelectrode where the current is correlated to the concentration of aparticular analyte and where the detected current gradually reduces butthe concentration of the analyte does not.

[0086] 2.0.0 General Methods

[0087] The present invention relates to the analysis of data obtained byuse of a sensing device for measuring the concentration of a targetanalyte present in a biological system. In preferred embodiments, thesensing device comprises a biosensor. In other preferred embodiments, asampling device is used to extract small amounts of a target analytefrom the biological system, and then sense and/or quantify theconcentration of the target analyte. Measurement with the biosensorand/or sampling with the sampling device can be carried out in acontinual manner. Continual measurement allows for closer monitoring oftarget analyte concentration fluctuations.

[0088] In the general method of the invention, a raw signal is obtainedfrom a sensing device, which signal is related to a target analytepresent in the biological system. The raw signal can be obtained usingany suitable sensing methodology including, for example, methods whichrely on direct contact of a sensing apparatus with the biologicalsystem; methods which extract samples from the biological system byinvasive, minimally invasive, and non-invasive sampling techniques,wherein the sensing apparatus is contacted with the extracted sample;methods which rely on indirect contact of a sensing apparatus with thebiological system; and the like. In preferred embodiments of theinvention, methods are used to extract samples from the biologicalsample using minimally invasive or non-invasive sampling techniques. Thesensing apparatus used with any of the above-noted methods can employany suitable sensing element to provide the signal including, but notlimited to, physical, chemical, electrochemical, photochemical,spectrophotometric, polarimetric, calorimetric, radiometric, or likeelements. In preferred embodiments of the invention, a biosensor is usedwhich comprises an electrochemical sensing element.

[0089] In another embodiment of the invention, a near-IR glucose sensingapparatus is used to detect blood glucose in a subject, and thusgenerate the raw signal. A number of near-IR glucose sensing devicessuitable for use in the present method are known in the art and arereadily available. For example, a near-IR radiation diffuse-reflectionlaser spectroscopy device is described in U.S. Pat. No. 5,267,152 toYang et al. Similar near-IR spectrometric devices are also described inU.S. Pat. No. 5,086,229 to Rosenthal et al. and U.S. Pat. No. 4,975,581to Robinson et al. These near-IR devices use traditional methods ofreflective or transmissive near infrared (near-IR) analysis to measureabsorbance at one or more glucose-specific wavelengths, and can becontacted with the subject at an appropriate location, such as afinger-tip, skin fold, eyelid, or forearm surface to obtain the rawsignal.

[0090] The raw signal obtained using any of the above-describedmethodologies is then converted into an analyte-specific value of knownunits to provide an interpretation of the signal obtained from thesensing device. The interpretation uses a mathematical transformation tomodel the relationship between a measured response in the sensing deviceand a corresponding analyte-specific value (in the present invention, aMixtures of Experts algorithm). Thus, a calibration step is used hereinto relate, for example, an electrochemical signal (detected by abiosensor), or near-IR absorbance spectra (detected with a near-IRdetector) with the concentration of a target analyte in a biologicalsystem.

[0091] The predicted analyte values can optionally be used in asubsequent step to control an aspect of the biological system. In oneembodiment, predicted analyte values are used to determine when, and atwhat level, a constituent should be added to the biological system inorder to control an aspect of the biological system. In a preferredembodiment, the analyte value can be used in a feedback control loop tocontrol a physiological effect in the biological system.

[0092] The above general methods can, of course, be used with a widevariety of biological systems, target analytes, and/or sensingtechniques. The determination of particularly suitable combinations iswithin the skill of the ordinarily skilled artisan when directed by theinstant disclosure. Although these methods are broadly applicable tomeasuring any chemical analyte and/or substance in a biological system,the invention is expressly exemplified for use in a non-invasive,transdermal sampling system which uses an electrochemical biosensor toquantify or qualify glucose or a glucose metabolite.

[0093] 2.1.0 Obtaining the Raw Signal.

[0094] The raw signal can be obtained using any sensing device that isoperatively contacted with the biological system. Such sensing devicescan employ physical, chemical, electrochemical, spectrophotometric,polarimetric, calorimetric, radiometric, or like measurement techniques.In addition, the sensing device can be in direct or indirect contactwith the biological system, or used with a sampling device whichextracts samples from the biological system using invasive, minimallyinvasive or non-invasive sampling techniques. In preferred embodiments,a minimally invasive or non-invasive sampling device is used to obtainsamples from the biological system, and the sensing device comprises abiosensor with an electrochemical sensing element.

[0095] The analyte can be any specific substance or component in abiological system that one is desirous of detecting and/or measuring ina chemical, physical, enzymatic, or optical analysis. Such analytesinclude, but are not limited to, amino acids, enzyme substrates orproducts indicating a disease state or condition, other markers ofdisease states or conditions, drugs of abuse, therapeutic and/orpharmacologic agents (e.g., theophylline, anti-HIV drugs, lithium,anti-epileptic drugs, cyclosporin, chemotherapeutics), electrolytes,physiological analytes of interest (e.g., urate/uric acid, carbonate,calcium, potassium, sodium, chloride, bicarbonate (CO₂), glucose, urea(blood urea nitrogen) lactate/lactic acid, hydroxybutyrate, cholesterol,triglycerides, creatine, creatinine, insulin, hematocrit, andhemoglobin), blood gases (carbon dioxide, oxygen, pH), lipids, heavymetals (e.g., lead, copper), and the like. In preferred embodiments, theanalyte is a physiological analyte of interest, for example glucose, ora chemical that has a physiological action, for example a drug orpharmacological agent.

[0096] In order to facilitate detection of the analyte, an enzyme can bedisposed in the collection reservoir, or, if several collectionreservoirs are used, the enzyme can be disposed in several or all of thereservoirs. The selected enzyme is capable of catalyzing a reaction withthe extracted analyte (e.g., glucose) to the extent that a product ofthis reaction can be sensed, e.g., can be detected electrochemicallyfrom the generation of a current which current is detectable andproportional to the concentration or amount of the analyte which isreacted. A suitable enzyme is glucose oxidase which oxidizes glucose togluconic acid and hydrogen peroxide. The subsequent detection ofhydrogen peroxide on an appropriate biosensor electrode generates twoelectrons per hydrogen peroxide molecule which create a current whichcan be detected and related to the amount of glucose entering thedevice. Glucose oxidase (GOx) is readily available commercially and haswell known catalytic characteristics. However, other enzymes can also beused, so long as they specifically catalyze a reaction with an analyteor substance of interest to generate a detectable product in proportionto the amount of analyte so reacted.

[0097] In like manner, a number of other analyte-specific enzyme systemscan be used in the invention, which enzyme systems operate on much thesame general techniques. For example, a biosensor electrode that detectshydrogen peroxide can be used to detect ethanol using an alcohol oxidaseenzyme system, or similarly uric acid with urate oxidase system, ureawith a urease system, cholesterol with a cholesterol oxidase system, andtheophylline with a xanthine oxidase system.

[0098] In addition, the oxidase enzyme (used for hydrogenperoxidase-based detection) can be replaced with another redox system,for example, the dehydrogenase-enzyme NAD-NADH, which offers a separateroute to detecting additional analytes. Dehydrogenase-based sensors canuse working electrodes made of gold or carbon (via mediated chemistry).Examples of analytes suitable for this type of monitoring include, butare not limited to, cholesterol, ethanol, hydroxybutyrate,phenylalanine, triglycerides, and urea. Further, the enzyme can beeliminated and detection can rely on direct electrochemical orpotentiometric detection of an analyte. Such analytes include, withoutlimitation, heavy metals (e.g., cobalt, iron, lead, nickel, zinc),oxygen, carbonate/carbon dioxide, chloride, fluoride, lithium, pH,potassium, sodium, and urea. Also, the sampling system described hereincan be used for therapeutic drug monitoring, for example, monitoringanti-epileptic drugs (e.g., phenytion), chemotherapy (e.g., adriamycin),hyperactivity (e.g., ritalin), and anti-organ-rejection (e.g.,cyclosporin).

[0099] In particularly preferred embodiments, a sampling device is usedto obtain continual transdermal or transmucosal samples from abiological system, and the analyte of interest is glucose. Morespecifically, a non-invasive glucose monitoring device is used tomeasure changes in glucose levels in an animal subject over a wide rangeof glucose concentrations. The sampling method is based on transdermalglucose extraction and the sensing method is based on electrochemicaldetection technology. The device can be contacted with the biologicalsystem continuously, and automatically obtains glucose samples in orderto measure glucose concentration at preprogrammed intervals.

[0100] Sampling is carried out continually by non-invasively extractingglucose through the skin of the patient using an iontophoretic current.More particularly, an iontophoretic current is applied to a surface ofthe skin of a subject. When the current is applied, ions or chargedmolecules pull along other uncharged molecules or particles such asglucose which are drawn into a collection reservoir placed on thesurface of the skin. The collection reservoir may comprise any ionicallyconductive material and is preferably in the form of a hydrogel which iscomprised of a hydrophilic material, water and an electrolyte. Thecollection reservoir may further contain an enzyme which catalyzes areaction between glucose and oxygen. The enzyme is preferably glucoseoxidase (GOx) which catalyzes the reaction between glucose and oxygenand results in the production of hydrogen peroxide. The hydrogenperoxide reacts at a catalytic surface of a biosensor electrode,resulting in the generation of electrons which create a detectablebiosensor current (raw signal). Based on the amount of biosensor currentcreated over a given period of time, a measurement is taken, whichmeasurement is related to the amount of glucose drawn into thecollection reservoir over a given period of time. In a preferredembodiment the reaction is allowed to continue until substantially allof the glucose in the collection reservoir has been subjected to areaction and is therefore no longer detectable, and the total biosensorcurrent generated is related to the concentration of glucose in thesubject.

[0101] When the reaction is complete, the process is repeated and asubsequent measurement is obtained. More specifically, the iontophoreticcurrent is again applied, glucose is drawn through the skin surface intothe collection reservoir, and the reaction is catalyzed in order tocreate a biosensor current. These sampling (extraction) and sensingoperations are integrated such that glucose from interstitial fluiddirectly beneath the skin surface is extracted into the hydrogelcollection pad where it contacts the GOx enzyme. The GOx enzyme convertsglucose and oxygen in the hydrogel to hydrogen peroxide which diffusesto a Pt-based sensor and reacts with the sensor to regenerate oxygen andform electrons. The electrons generate an electrical signal that can bemeasured, analyzed, and correlated to blood glucose.

[0102] A generalized method for continual monitoring of a physiologicalanalyte is disclosed in International Publication No. WO 97/24059,published Jul. 10, 1997, which publication is incorporated herein byreference. As noted in that publication, the analyte is extracted into areservoir containing a hydrogel which is preferably comprised of ahydrophilic material of the type described in International PublicationNo. WO 97/02811, published Jan. 30, 1997, which publication isincorporated herein by reference. Suitable hydrogel materials includepolyethylene oxide, polyacrylic acid, polyvinylalcohol and relatedhydrophilic polymeric materials combined with water to form an aqueousgel.

[0103] In the above non-invasive glucose monitoring device, a biosensorelectrode is positioned on a surface of the hydrogel opposite thesurface contacting the skin. The sensor electrode acts as a detectorwhich detects current generated by hydrogen peroxide in the redoxreaction, or more specifically detects current which is generated by theelectrons generated by the redox reaction catalyzed by the platinumsurface of the electrode. The details of such electrode assemblies anddevices for iontophoretic extraction of glucose are disclosed inInternational Publication No. WO 96/00110, published Jan. 4, 1996, andInternational Publication No. WO 97/10499, published Mar. 5, 1997, whichpublications are also incorporated herein by reference.

[0104] Referring now to FIGS. 1A and 1B, one example of an iontophoreticcollection reservoir and electrode assembly for use in a transdermalsensing device is generally indicated at 2. The assembly comprises twoiontophoretic collection reservoirs, 4 and 6, each having a conductivemedium 8, and 10 (preferably hydrogel pads), respectively disposedtherein. First (12) and second (14) ring-shaped iontophoretic electrodesare respectively contacted with conductive medium 8 and 10. The firstiontophoretic electrode 12 surrounds three biosensor electrodes whichare also contacted with the conductive medium 8, a working electrode 16,a reference electrode 18, and a counter electrode 20. A guard ring 22separates the biosensor electrodes from the iontophoretic electrode 12to minimize noise from the iontophoretic circuit. Conductive contactsprovide communication between the electrodes and an associated powersource and control 30 means as described in detail below. A similarbiosensor electrode arrangement can be contacted with the conductivemedium 10, or the medium can not have a sensor means contactedtherewith.

[0105] Referring now to FIG. 2, the iontophoretic collection reservoirand electrode assembly 2 of FIGS. 1A and 1B is shown in exploded view incombination with a suitable iontophoretic sampling device housing 32.The housing can be a plastic case or other suitable structure whichpreferably is configured to be worn on a subjects arm in a mannersimilar to a wrist watch. As can be seen, conductive media 8 and 10(hydrogel pads) are separable from the assembly 2; however, when theassembly 2 and the housing 32 are assembled to provide an operationaliontophoretic sampling device 30, the media are in contact with theelectrodes to provide a electrical contact therewith.

[0106] A power source (e.g., one or more rechargeable or nonrechargeablebatteries) can be disposed within the housing 32 or within the straps 34which hold the device in contact with a skin or mucosal surface of asubject. In use, an electric potential (either direct current or a morecomplex waveform) is applied between the two iontophoretic electrodes 12and 14 such that current flows from the first iontophoretic electrode12, through the first conductive medium 8 into the skin or mucosalsurface, and then back out through the second conductive medium 10 tothe second iontophoretic electrode 14. The current flow is sufficient toextract substances including an analyte of interest through the skininto one or both of collection reservoirs 4 and 6. The electricpotential may be applied using any suitable technique, for example, theapplied current density may be in the range of about 0.01 to 0.5 mA/cm².In a preferred embodiment, the device is used for continual orcontinuous monitoring, and the polarity of iontophoretic electrodes 12and 14 is alternated at a rate of about one switch every 10 seconds toabout one switch every hour so that each electrode is alternately acathode or an anode. The housing 32 can further include an optionaltemperature sensing element (e.g., a thermistor, thermometer, orthermocouple device) which monitors the temperature at the collectionreservoirs to enable temperature correction of sensor signals. Thehousing can also include an optional conductance sensing element (e.g.,an integrated pair of electrodes) which monitors conductance at the skinor mucosal surface to enable data screening correction or invalidationof sensor signals.

[0107] After a suitable iontophoretic extraction period, one or both ofthe sensor electrode sets can be activated in order to detect extractedsubstances including the analyte of interest. Operation of theiontophoretic sampling device 30 can be controlled by a controller 36(e.g., a microprocessor), which interfaces with the iontophoreticelectrodes, the sensor electrodes, the power supply, the optionaltemperature and/or conductance sensing elements, a display and otherelectronics. For example, the controller 36 can include a programmable acontrolled circuit source/sink drive for driving the iontophoreticelectrodes. Power and reference voltage are provided to the sensorelectrodes, and signal amplifiers can be used to process the signal fromthe working electrode or electrodes. In general, the controllerdiscontinues the iontophoretic current drive during sensing periods. Asensor confidence loop can be provided for continually monitoring thesampling system to insure proper operations.

[0108] User control can be carried out using push buttons located on thehousing 32, and an optional liquid crystal display (LCD) can providevisual prompts, readouts and visual alarm indications. Themicroprocessor generally uses a series of program sequences to controlthe operations of the sampling device, which program sequences can bestored in the microprocessor's read only memory (ROM). Embedded software(firmware) controls activation of measurement and display operations,calibration of analyte readings, setting and display of high and lowanalyte value alarms, display and setting of time and date functions,alarm time, and display of stored readings. Sensor signals obtained fromthe sensor electrodes can be processed before storage and display by oneor more signal processing functions or algorithms which are stored inthe embedded software. The microprocessor can also include anelectronically erasable, programmable, read only memory (EEPROM) forstoring calibration parameters, user settings and all downloadablesequences. A serial communications port allows the device to communicatewith associated electronics, for example, wherein the device is used ina feedback control application to control a pump for delivery of amedicament.

[0109] Further, the sampling system can be pre-programmed to beginexecution of its signal measurements (or other functions) at adesignated time. One application of this feature is to have the samplingsystem in contact with a subject and to program the sampling system tobegin sequence execution during the night so that it is available forcalibration immediately upon waking. One advantage of this feature isthat it removes any need to wait for the sampling system to warm-upbefore calibrating it.

[0110] 2.1.1 Exemplary Embodiments of the Sampling System

[0111] An exemplary method and apparatus for sampling small amounts ofan analyte via transdermal methods is described below in further detail.The method and apparatus are used to detect and/or quantify theconcentration of a target analyte present in a biological system. Thissampling is carried out in a continual manner, and quantification ispossible even when the target analyte is extracted in sub-millimolarconcentrations. Although the method and apparatus are broadly applicableto sampling any chemical analyte and/or substance, the sampling systemis expressly exemplified for use in transdermal sampling and quantifyingor qualifying glucose or a glucose metabolite.

[0112] Accordingly, in one aspect, an automatic sampling system is usedto monitor levels of glucose in a biological system. The method can bepracticed using a sampling system (device) which transdermally extractsglucose from the system, in this case, an animal subject. Transdermalextraction is carried out by applying an electrical current orultrasonic radiation to a tissue surface at a collection site. Theelectrical current or ultrasonic radiation is used to extract smallamounts of glucose from the subject into a collection reservoir. Thecollection reservoir is in contact with a biosensor which provides formeasurement of glucose concentration in the subject.

[0113] In the practice, a collection reservoir is contacted with atissue surface, for example, on the stratum corneum of a patient's skin.An electrical or ultrasonic force is then applied to the tissue surfacein order to extract glucose from the tissue into the collectionreservoir. Extraction is carried out continually over a period of about1-24 hours, or longer. The collection reservoir is analyzed, at leastperiodically, to measure glucose concentration therein. The measuredvalue correlates with the subject's blood glucose level.

[0114] More particularly, one or more collection reservoirs are placedin contact with a tissue surface on a subject. The collection reservoirsare also contacted with an electrode which generates a current (forreverse iontophoretic extraction) or with a source of ultrasonicradiation such as a transducer (for sonophoretic extraction) sufficientto extract glucose from the tissue into the collection reservoir.

[0115] The collection reservoir contains an ionically conductive liquidor liquid-containing medium. The conductive medium is preferably ahydrogel which can contain ionic substances in an amount sufficient toproduce high ionic conductivity. The hydrogel is formed from a solidmaterial (solute) which, when combined with water, forms a gel by theformation of a structure which holds water including interconnectedcells and/or network structure formed by the solute. The solute may be anaturally occurring material such as the solute of natural gelatin whichincludes a mixture of proteins obtained by the hydrolysis of collagen byboiling skin, ligaments, tendons and the like. However, the solute orgel forming material is more preferably a polymer material (including,but not limited to, polyethylene oxide, polyvinyl alcohol, polyacrylicacid, polyacrylamidomethylpropanesulfonate and copolymers thereof, andpolyvinyl pyrrolidone) present in an amount in the range of more than0.5% and less than 40% by weight, preferably 8 to 12% by weight when ahumectant is also added, and preferably about 15 to 20% by weight whenno humectant is added. Additional materials may be added to thehydrogel, including, without limitation, electrolyte (e.g., a salt),buffer, tackifier, humectant, biocides, preservatives and enzymestabilizers. Suitable hydrogel formulations are described inInternational Publication Nos. WO 97/02811, published Jan. 30, 1997, andWO 96/00110, published Jan. 4, 1996, each of which publications areincorporated herein by reference in their entireties.

[0116] Since the sampling system must be operated at very low(electrochemical) background noise levels, the collection reservoir mustcontain an ionically conductive medium that does not include significantelectrochemically sensitive components and/or contaminants. Thus, thepreferred hydrogel composition described hereinabove is formulated usinga judicious selection of materials and reagents which do not addsignificant amounts of electrochemical contaminants to the finalcomposition.

[0117] In order to facilitate detection of the analyte, an enzyme isdisposed within the one or more collection reservoirs. The enzyme iscapable of catalyzing a reaction with the extracted analyte (in thiscase glucose) to the extent that a product of this reaction can besensed, e.g., can be detected electrochemically from the generation of acurrent which current is detectable and proportional to the amount ofthe analyte which is reacted. A suitable enzyme is glucose oxidase whichoxidizes glucose to gluconic acid and hydrogen peroxide. The subsequentdetection of hydrogen peroxide on an appropriate biosensor electrodegenerates two electrons per hydrogen peroxide molecule which create acurrent which can be detected and related to the amount of glucoseentering the device (see FIG. 1). Glucose oxidase (GOx) is readilyavailable commercially and has well known catalytic characteristics.However, other enzymes can also be used, so long as they specificallycatalyze a reaction with an analyte, or derivative thereof (or substanceof interest), to generate a detectable product in proportion to theamount of analyte so reacted.

[0118] In like manner, a number of other analyte-specific enzyme systemscan be used in the sampling system, which enzyme systems operate on muchthe same general techniques. For example, a biosensor electrode thatdetects hydrogen peroxide can be used to detect ethanol using an alcoholoxidase enzyme system, or similarly uric acid with urate oxidase system,cholesterol with a cholesterol oxidase system, and theophylline with axanthine oxidase system.

[0119] The biosensor electrode must be able to detect the glucoseanalyte extracted into the one or more collection reservoirs even whenpresent at nominal concentration levels. In this regard, conventionalelectrochemical detection systems which utilize glucose oxidase (GOx) tospecifically convert glucose to hydrogen peroxide, and then detect withan appropriate electrode, are only capable of detecting the analyte whenpresent in a sample in at least mM concentrations. In contrast, thesampling system allows sampling and detection of small amounts ofanalyte from the subject, wherein the analyte is detected atconcentrations on the order of 2 to 4 orders of magnitude lower (e.g.,μM concentration in the reservoir) than presently detectable withconventional systems.

[0120] Accordingly, the biosensor electrode must exhibit substantiallyreduced background current relative to prior such electrodes. In oneparticularly preferred embodiment, an electrode is provided whichcontains platinum (Pt) and graphite dispersed within a polymer matrix.The electrode exhibits the following features, each of which areessential to the effective operation of the biosensor: backgroundcurrent in the electrode due to changes in the Pt oxidation state andelectrochemically sensitive contaminants in the electrode formulation issubstantially reduced; and catalytic activity (e.g., non-electrochemicalhydrogen peroxide decomposition) by the Pt in the electrode is reduced.

[0121] The Pt-containing electrode is configured to provide a geometricsurface area of about 0.1 to 3 cm², preferably about 0.5 to 2 cm², andmore preferably about 1 cm². This particular configuration is scaled inproportion to the collection area of the collection reservoir used inthe sampling system, throughout which the extracted analyte and/or itsreaction products will be present. The electrode is specially formulatedto provide a high signal-to-noise ratio (S/N ratio) for this geometricsurface area not heretofore available with prior Pt-containingelectrodes. For example, a Pt-containing electrode constructed for usein the sampling system and having a geometric area of about 1 cm²preferably has a background current on the order of about 20 nA or less(when measured with buffer solution at 0.6V), and has high sensitivity(e.g., at least about 60 nA/μM of H₂O₂ in buffer at 0.6V). In likemanner, an electrode having a geometric area of about 0.1 cm² preferablyhas a background-current of about 2 nA or less and sensitivity of atleast about 6 nA/μM of H₂O₂; and an electrode having a geometric area ofabout 3 cm² preferably has a background current of about 60 nA or lessand sensitivity of at least about 180 nA/μM of H₂O₂, both as measured inbuffer at 0.6V. These features provide for a high S/N ratio, for examplea S/N ratio of about 3 or greater. The electrode composition isformulated using analytical- or electronic-grade reagents and solventswhich ensure that electrochemical and/or other residual contaminants areavoided in the final composition, significantly reducing the backgroundnoise inherent in the resultant electrode. In particular, the reagentsand solvents used in the formulation of the electrode are selected so asto be substantially free of electrochemically active contaminants (e.g.,anti-oxidants), and the solvents in particular are selected for highvolatility in order to reduce washing and cure times.

[0122] The Pt powder used to formulate the electrode composition is alsosubstantially free from impurities, and the Pt/graphite powders areevenly distributed within the polymer matrix using, for example,co-milling or sequential milling of the Pt and graphite. Alternatively,prior to incorporation into the polymer matrix, the Pt can be sputteredonto the graphite powder, colloidal Pt can be precipitated onto thegraphite powder (see, e.g., U.K. patent application number GB 2,221,300,published Jan. 31, 1990, and references cited therein), or the Pt can beadsorbed onto the graphite powder to provide an even distribution of Ptin contact with the conductive graphite. In order to improve the S/Nratio of the electrode, the Pt content in the electrode is lowerrelative to prior Pt or Pt-based electrodes. In a preferred embodiment,the overall Pt content is about 3-7% by weight. Although decreasing theoverall amount of Pt may reduce the sensitivity of the electrode, theinventors have found that an even greater reduction in background noiseis also achieved, resulting in a net improvement in signal-to-noisequality.

[0123] The Pt/graphite matrix is supported by a suitable binder, such asan electrochemically inert polymer or resin binder, which is selectedfor good adhesion and suitable coating integrity. The binder is alsoselected for high purity, and for absence of components withelectrochemical background. In this manner, no electrochemicallysensitive contaminants are introduced into the electrode composition byway of the binder. A large number of suitable such binders are known inthe art and are commercially available, including, without limitation,vinyl, acrylic, epoxy, phenoxy and polyester polymers, provided that thebinder or binders selected for the formulation are adequately free ofelectroactive impurities.

[0124] The Pt/graphite biosensor electrodes formulated above exhibitreduced catalytic activity (e.g., passive or non-electrochemicalhydrogen peroxide degradation) relative to prior Pt-based electrodesystems, and thus have substantially improved signal-to-noise quality.In preferred Pt/graphite electrode compositions, the biosensor exhibitsabout 10-25% passive hydrogen peroxide degradation.

[0125] Once formulated, the electrode composition is affixed to asuitable nonconductive surface which may be any rigid or flexiblematerial having appropriate insulating and/or dielectric properties. Theelectrode composition can be affixed to the surface in any suitablepattern or geometry, and can be applied using various thin filmtechniques, such as sputtering, evaporation, vapor phase deposition, orthe like; or using various thick film techniques, such as filmlaminating, electroplating, or the like. Alternatively, the compositioncan be applied using screen printing, pad printing, inkjet methods,transfer roll printing, or similar techniques. Preferably, the electrodeis applied using a low temperature screen print onto a polymericsubstrate. The screening can be carried out using a suitable mesh,ranging from about 100-400 mesh.

[0126] As glucose is transdermally extracted into the collectionreservoir, the analyte reacts with the glucose oxidase within thereservoir to produce hydrogen peroxide. The presence of hydrogenperoxide generates a current at the biosensor electrode that is directlyproportional to the amount of hydrogen peroxide in the reservoir. Thiscurrent provides a signal which can be detected and interpreted by anassociated system controller to provide a glucose concentration valuefor display. In particular embodiments, the detected current can becorrelated with the subject's blood glucose concentration so that thesystem controller may display the subject's actual blood glucoseconcentration as measured by the sampling system. For example, thesystem can be calibrated to the subject's actual blood glucoseconcentration by sampling the subject's blood during a standard glucosetolerance test, and analyzing the blood glucose using both a standardblood glucose monitor and the sampling system. In this manner,measurements obtained by the sampling system can be correlated to actualvalues using known statistical techniques.

[0127] In one preferred embodiment, the automatic sampling systemtransdermally extracts the sample in a continual manner over the courseof a 1-24 hour period, or longer, using reverse iontophoresis. Moreparticularly, the collection reservoir contains an ionically conductivemedium, preferably the hydrogel medium described hereinabove. A firstiontophoresis electrode is contacted with the collection reservoir(which is in contact with a target tissue surface), and a secondiontophoresis electrode is contacted with either a second collectionreservoir in contact with the tissue surface, or some other ionicallyconductive medium in contact with the tissue. A power source provides anelectric potential between the two electrodes to perform reverseiontophoresis in a manner known in the art. As discussed above, thebiosensor selected to detect the presence, and possibly the level, ofthe target analyte (glucose) within a reservoir is also in contact withthe reservoir.

[0128] In practice, an electric potential (either direct current or amore complex waveform) is applied between the two iontophoresiselectrodes such that current flows from the first electrode through thefirst conductive medium into the skin, and back out from the skinthrough the second conductive medium to the second electrode. Thiscurrent flow extracts substances through the skin into the one or morecollection reservoirs through the process of reverse iontophoresis orelectroosmosis. The electric potential may be applied as described inInternational Publication No. WO 96/00110, published Jan. 4, 1996.

[0129] As an example, to extract glucose, the applied electrical currentdensity on the skin or tissue is preferably in the range of about 0.01to about 2 mA/cm². In a preferred embodiment, in order to facilitate theextraction of glucose, electrical energy is applied to the electrodes,and the polarity of the electrodes is alternated at a rate of about oneswitch every 7.5 minutes (for a 15 minute extraction period), so thateach electrode is alternately a cathode or an anode. The polarityswitching can be manual or automatic.

[0130] Any suitable iontophoretic electrode system can be employed,however it is preferred that a silver/silver chloride (Ag/AgCl)electrode system is used. The iontophoretic electrodes are formulatedusing two critical performance parameters: (1) the electrodes arecapable of continual operation for extended periods, preferably periodsof up to 24 hours or longer; and (2) the electrodes are formulated tohave high electrochemical purity in order to operate within the presentsystem which requires extremely low background noise levels. Theelectrodes must also be capable of passing a large amount of charge overthe life of the electrodes.

[0131] In an alternative embodiment, the sampling device can operate inan alternating polarity mode necessitating the presence of a first andsecond bimodal electrodes (FIG. 5, 540 and 541) and two collectionreservoirs (FIG. 5, 547 and 548). Each bi-modal electrode (FIG. 4, 430;FIG. 5, 540 and 541) serves two functions depending on the phase of theoperation: (1) an electro-osmotic electrode (or iontophoretic electrode)used to electrically draw analyte from a source into a collectionreservoir comprising water and an electrolyte, and to the area of theelectrode subassembly; and (2) as a counter electrode to the firstsensing electrode at which the chemical compound is catalyticallyconverted at the face of the sensing electrode to produce an electricalsignal.

[0132] The reference (FIG. 5, 544 and 545; FIG. 4, 432) and sensingelectrodes (FIG. 5, 542 and 543; FIG. 4, 431), as well as, the bimodalelectrode (FIG. 5, 540 and 541; FIG. 4, 430) are connected to a standardpotentiostat circuit during sensing. In general, practical limitationsof the system require that the bimodal electrode will not act as both acounter and iontophoretic electrode simultaneously.

[0133] The general operation of an iontophoretic sampling system is thecyclical repetition of two phases: (1) a reverse-iontophoretic phase,followed by a (2) sensing phase. During the reverse iontophoretic phase,the first bimodal electrode (FIG. 5, 540) acts as an iontophoreticcathode and the second bimodal electrode (FIG. 5, 541) acts as aniontophoretic anode to complete the circuit. Analyte is collected in thereservoirs, for example, a hydrogel (FIG. 5, 547 and 548). At the end ofthe reverse iontophoretic phase, the iontophoretic current is turnedoff. During the sensing phase, in the case of glucose, a potential isapplied between the reference electrode (FIG. 5, 544) and the sensingelectrode (FIG. 5, 542). The chemical signal reacts catalytically on thecatalytic face of the first sensing electrode (FIG. 5, 542) producing anelectrical current, while the first bi-modal electrode (FIG. 5, 540)acts as a counter electrode to complete the electrical circuit.

[0134] At the end of the sensing phase, the next iontophoresis phasebegins. The polarity of the iontophoresis current is reversed in thiscycle relative to the previous cycle, so that the first bi-modalelectrode (FIG. 5, 540) acts as an iontophoretic anode and the secondbi-modal electrode (FIG. 5, 541) acts as an iontophoretic cathode tocomplete the circuit. At the end of the iontophoretic phase, the sensoris activated. The chemical signal reacts catalytically on the catalyticface of the second sensing electrode (FIG. 5, 543) producing anelectrical current, while the second bi-modal electrode (FIG. 5, 541)acts as a counter electrode to complete the electrical circuit.

[0135] The iontophoretic and sensing phases repeat cyclically with thepolarity of the iontophoretic current alternating between each cycle.This results in pairs of readings for the signal, that is, one signalobtained from a first iontophoretic and sensing phase and a secondsignal obtained from the second phase. These two values can be used (i)independently as two signals, (ii) as a cumulative (additive) signal, or(iii) the signal values can be added and averaged.

[0136] If two active reservoirs are used for analyte detection (forexample, where both hydrogels contain the GOx enzyme), a sensorconsistency check can be employed that detects whether the signals fromthe reservoirs are changing in concert with one another. This checkcompares the percentage change from the calibration signal for eachreservoir, then calculates the difference in percentage change of thesignal between the two reservoirs. If this difference is greater than apredetermined threshold value (which is commonly empiricallydetermined), then the signals are said not to be tracking one anotherand the data point related to the two signals can be, for example,ignored.

[0137] The electrode described is particularly adapted for use inconjunction with a hydrogel collection reservoir system for monitoringglucose levels in a subject through the reaction of collected glucosewith the enzyme glucose oxidase present in the hydrogel matrix.

[0138] The bi-modal electrode is preferably comprised of Ag/AgCl. Theelectrochemical reaction which occurs at the surface of this electrodeserves as a facile source or sink for electrical current. This propertyis especially important for the iontophoresis function of the electrode.Lacking this reaction, the iontophoresis current could cause thehydrolysis of water to occur at the iontophoresis electrodes causing pHchanges and possible gas bubble formation. The pH changes to acidic orbasic pH could cause skin irritation or burns. The ability of an Ag/AgClelectrode to easily act as a source of sink current is also an advantagefor its counter electrode function. For a three electrodeelectrochemical cell to function properly, the current generationcapacity of the counter electrode must not limit the speed of thereaction at the sensing electrode. In the case of a large sensingelectrode, the ability of the counter electrode to sourceproportionately larger currents is required.

[0139] The design of the sampling system provides for a larger sensingelectrode (see for example, FIG. 4) than previously designed.Consequently, the size of the bimodal electrode must be sufficient sothat when acting as a counter electrode with respect to the sensingelectrode the counter electrode does not become limiting the rate ofcatalytic reaction at the sensing electrode catalytic surface.

[0140] Two methods exist to ensure that the counter electrode does notlimit the current at the sensing electrode: (1) the bi-modal electrodeis made much larger than the sensing electrode, or (2) a facile counterreaction is provided.

[0141] During the reverse iontophoretic phase, the power source providesa current flow to the first bi-modal electrode to facilitate theextraction of the chemical signal into the reservoir. During the sensingphase, the power source is used to provide voltage to the first sensingelectrode to drive the conversion of chemical signal retained inreservoir to electrical signal at the catalytic face of the sensingelectrode. The power source also maintains a fixed potential at theelectrode where, for example hydrogen peroxide is converted to molecularoxygen, hydrogen ions, and electrons, which is compared with thepotential of the reference electrode during the sensing phase. While onesensing electrode is operating in the sensing mode it is electricallyconnected to the adjacent bimodal electrode which acts as a counterelectrode at which electrons generated at is the sensing electrode areconsumed.

[0142] The electrode sub-assembly can be operated by electricallyconnecting the bimodal electrodes such that each electrode is capable offunctioning as both an iontophoretic electrode and counter electrodealong with appropriate sensing electrode(s) and reference electrodes),to create standard potentiostat circuitry.

[0143] A potentiostat is an electrical circuit used in electrochemicalmeasurements in three electrode electrochemical cells. A potential isapplied between the reference electrode and the sensing electrode. Thecurrent generated at the sensing electrode flows through circuitry tothe counter electrode (i.e., no current flows through the referenceelectrode to alter its equilibrium potential). Two independentpotentiostat circuits can be used to operate the two biosensors. For thepurpose of the present sampling system, the electrical current measuredat the sensing electrode subassembly is the current that is correlatedwith an amount of chemical signal.

[0144] With regard to continual operation for extended periods of time,Ag/AgCl electrodes are provided herein which are capable of repeatedlyforming a reversible couple which operates without unwantedelectrochemical side reactions (which could give rise to changes in pH,and liberation of hydrogen and oxygen due to water hydrolysis). TheAg/AgCl electrodes of the present sampling system are thus formulated towithstand repeated cycles of current passage in the range of about 0.01to 1.0 mA per cm² of electrode area. With regard to high electrochemicalpurity, the Ag/AgCl components are dispersed within a suitable polymerbinder to provide an electrode composition which is not susceptible toattack (e.g., plasticization) by components in the collection reservoir,e.g., the hydrogel composition. The electrode compositions are alsoformulated using analytical- or electronic-grade reagents and solvents,and the polymer binder composition is selected to be free ofelectrochemically active contaminants which could diffuse to thebiosensor to produce a background current.

[0145] Since the Ag/AgCl iontophoretic electrodes must be capable ofcontinual cycling over extended periods of time, the absolute amounts ofAg and AgCl available in the electrodes, and the overall Ag/AgClavailability ratio, can be adjusted to provide for the passage of highamounts of charge. Although not limiting in the sampling systemdescribed herein, the Ag/AgCl ratio can approach unity. In order tooperate within the preferred system which uses a biosensor having ageometric area of 0.1 to 3 cm², the iontophoretic electrodes areconfigured to provide an approximate electrode area of 0.3 to 1.0 cm²,preferably about 0.85 cm². These electrodes provide for reproducible,repeated cycles of charge passage at current densities ranging fromabout 0.01 to 1.0 mA/cm² of electrode area. More particularly,electrodes constructed according to the above formulation parameters,and having an approximate electrode area of 0.85 cm², are capable of areproducible total charge passage (in both anodic and cathodicdirections) of 270 mC, at a current of about 0.3 mA (current density of0.35 mA/cm²) for 48 cycles in a 24 hour period.

[0146] Once formulated, the Ag/AgCl electrode composition is affixed toa suitable rigid or flexible nonconductive surface as described abovewith respect to the biosensor electrode composition. A silver (Ag)underlayer is first applied to the surface in order to provide uniformconduction. The Ag/AgCl electrode composition is then applied over theAg underlayer in any suitable pattern or geometry using various thinfilm techniques, such as sputtering, evaporation, vapor phasedeposition, or the like, or using various thick film techniques, such asfilm laminating, electroplating, or the like. Alternatively, the Ag/AgClcomposition can be applied using screen printing, pad printing, inkjetmethods, transfer roll printing, or similar techniques. Preferably, boththe Ag underlayer and the Ag/AgCl electrode are applied using a lowtemperature screen print onto a polymeric substrate. This lowtemperature screen print can be carried out at about 125 to 160° C., andthe screening can be carried out using a suitable mesh, ranging fromabout 100-400 mesh.

[0147] In another preferred embodiment, the automatic sampling systemtransdermally extracts the sample in a continual manner over the courseof a 1-24 hour period, or longer, using sonophoresis. More particularly,a source of ultrasonic radiation is coupled to the collection reservoirand used to provide sufficient perturbation of the target tissue surfaceto allow passage of the analyte (glucose) across the tissue surface. Thesource of ultrasonic radiation provides ultrasound frequencies ofgreater than about 10 MHz, preferably in the range of about 10 to 50MHz, most preferably in the range of about 15 to 25 MHz. It should beemphasized that these ranges are intended to be merely illustrative ofthe preferred embodiment; in some cases higher or lower frequencies maybe used.

[0148] The ultrasound may be pulsed or continuous, but is preferablycontinuous when lower frequencies are used. At very high frequencies,pulsed application will generally be preferred so as to enabledissipation of generated heat. The preferred intensity of the appliedultrasound is less than about 5.0 W/cm², more preferably is in the rangeof about 0.01 to 5.0 W/cm², and most preferably is in the range of 0.05to 3.0 W/cm².

[0149] Virtually any type of device may be used to administer theultrasound, providing that the device is capable of producing thesuitable frequency ultrasonic waves required by the sampling system. Anultrasound device will typically have a power source such as a smallbattery, a transducer, and a means to attach the system to the samplingsystem collection reservoir. Suitable sonophoresis sampling systems aredescribed in International Publication No. WO 91/12772, published Sep.5, 1991, the disclosure of which is incorporated herein by reference.

[0150] As ultrasound does not transmit well in air, a liquid medium isgenerally needed in the collection reservoir to efficiently and rapidlytransmit ultrasound between the ultrasound applicator and the tissuesurface.

[0151] Referring now to FIG. 3, an exploded view of the key componentsfrom a preferred embodiment of an autosensor is presented. The samplingsystem components include two biosensor/iontophoretic electrodeassemblies, 304 and 306, each of which have an annular iontophoreticelectrode, respectively indicated at 308 and 310, which encircles abiosensor 312 and 314. The electrode assemblies 304 and 306 are printedonto a polymeric substrate 316 which is maintained within a sensor tray318. A collection reservoir assembly 320 is arranged over the electrodeassemblies, wherein the collection reservoir assembly comprises twohydrogel inserts 322 and 324 retained by a gel retaining layer 326.

[0152] Referring now to FIG. 9, an exploded view of the key componentsfrom another embodiment of an autosensor for use in an iontophoreticsampling device is presented. The sampling system components include,but are not limited to, the following: a sensor-to-tray assemblycomprising two bimodal electrode assemblies and a support tray 904; twoholes 906 to insure proper alignment of the support tray in the samplingdevice; a plowfold liner 908 used to separate the sensors from thehydrogels 912 (for example, during storage); a gel retaining layer 910;a mask layer 914 (where the gel retaining layer, hydrogels, and masklayer form a collection assembly, which can, for example, be alaminate); and a patient liner 916.

[0153] The components shown in exploded view in FIGS. 3 and 9 areintended for use in, for example, an automatic sampling device which isconfigured to be worn like an ordinary wristwatch. As described inInternational Publication No. WO 96/00110, published Jan. 4, 1996, thewristwatch housing (not shown) contains conductive leads whichcommunicate with the iontophoretic electrodes and the biosensorelectrodes to control cycling and provide power to the iontophoreticelectrodes, and to detect electrochemical signals produced at thebiosensor electrode surfaces. The wristwatch housing can further includesuitable electronics (e.g., microprocessor, memory, display and othercircuit components) and power sources for operating the automaticsampling system.

[0154] Modifications and additions to the embodiments of FIGS. 3 and 9will be apparent to those skilled in the art in light of the teachingsof the present specification. The laminates and collection assembliesdescribed herein are suitable for use as consumable components in aniontophoretic sampling device.

[0155] In one aspect, the electrode assemblies can include bimodalelectrodes as shown in FIG. 4 and described above.

[0156] Modifications and additions to the embodiments shown in FIGS. 3and 9 will be apparent to those skilled in the art.

[0157] 2.2.0 Converting to an Analyte-Specific Value.

[0158] The raw signal is then converted into an analyte-specific valueusing a calibration step which correlates the signal obtained from thesensing device with the concentration of the analyte present in thebiological system. A wide variety of calibration techniques can be usedto interpret such signals. These calibration techniques applymathematical, statistical and/or pattern recognition techniques to theproblem of signal processing in chemical analyses, for example, usingneural networks, genetic algorithm signal processing, linear regression,multiple-linear regression, or principal components analysis ofstatistical (test) measurements.

[0159] One method of calibration involves estimation techniques. Tocalibrate an instrument using estimation techniques, it is necessary tohave a-set of exemplary measurements with known concentrations referredto as the calibration set (e.g., reference set). This set consists of Ssamples, each with m instrument variables contained in an S by m matrix(X), and an S by 1 vector (y), containing the concentrations. If apriori information indicates the relationship between the measurementand concentration is linear, the calibration will attempt to determinean S by 1 transformation or mapping (b), such that y=Xb, is an optimalestimate of y according to a predefined criteria. Numerous suitableestimation techniques useful in the practice of the invention are knownin the art. These techniques can be used to provide correlation factors(e.g., constants), which correlation factors are then used in amathematical transformation to obtain a measurement value indicative ofthe concentration of analyte present in the biological system at thetimes of measurement.

[0160] In one particular embodiment, the calibration step can be carriedout using artificial neural networks or genetic algorithms. Thestructure of a particular neural network algorithm used in the practiceof the invention can vary widely; however, the network should contain aninput layer, one or more hidden layers, and one output layer. Suchnetworks can be trained on a test data set, and then applied to apopulation. There are an infinite number of suitable network types,transfer functions, training criteria, testing and application methodswhich will occur to the ordinarily skilled artisan upon reading theinstant specification.

[0161] The iontophoretic glucose sampling device described hereinabovetypically uses one or more “active” collection reservoirs (e.g., eachcontaining the GOx enzyme) to obtain measurements. In one embodiment,two active collection reservoirs are used. An input value can beobtained from these reserviors by, for example, taking an averagebetween signals from the reservoirs for each measurement time point orusing a summed value. Such inputs are discussed in greater detail below.In another embodiment, a second collection reservoir can be providedwhich does not contain, for example, the GOx enzyme. This secondreservoir can serve as an internal reference (blank) for the sensingdevice, where a biosensor is used to measure the “blank” signal from thereference reservoir which signal can then be used in, for example, ablank subtraction step.

[0162] In the context of such a sampling device an algorithm, in apreferred embodiment a Mixtures of Experts algorithm, could use thefollowing inputs to provide a blood glucose measurement: time (forexample, time since monitor was applied to a subject, and/or time sincecalibration); signal from an active reservoir; signal from a blankreservoir; averaged (or a cumulative) signal from two active reservoirs;calibration time; skin temperature; voltage; normalized background; rawdata current; peak or minimum value of a selected input, e.g., current,averged signal, calibrated signal; discrete value points of a selectedinput, e.g., current, averged signal, calibrated signal; integralavgerage temperature, initial temperature, or any discrete timetemperature; skin conductivity, including, but not limited to, sweatvalue, iontophoretic voltage, baseline value, normalized baseline value,other background values; relative change in biosensor current oriontophoretic voltage (relative to calibration) as an indicator ofdecay; alternate integration ranges for calculating nanocoulomb (nC)values, e.g., using an entire biosensor time interval, or alternativeranges of integration (for example, using discrete time points insteadof ranges, break out intervals from the total sampling time interval, orfull integration of the interval plus partial integration of selectedportions of the interval); and, when operating in the training mode,measured glucose (use of exemplary inputs are presented in Examples 1and 2). Further, a calibration ratio check is described in Example 4that is useful to insure that the calibration has been efficacious, andthat the calibration demonstrates a desired level of sensitivity of thesampling system.

[0163] 2.3.0 Predicting Measurements

[0164] The analyte-specific values obtained using the above techniquesare used herein to predict target analyte concentrations in a biologicalsystem using a Mixtures of Experts (MOE) analysis.

[0165] The Mixtures of Experts algorithm breaks up a non-linearprediction equation into several linear prediction equations(“Experts”). An “Expert” routine is then used to switch between thedifferent linear equations. In the equations presented below, the w(weighting) factor determines the switch by weighting the differentExperts with a number between 0 and 1, with the restriction that:${\sum\limits_{i = 1}^{n}w_{i}} = 1$

[0166] The Mixtures of Experts algorithm of the present invention isbased on the ideal case presented in Equation 1, where the individualexperts have a linear form: $\begin{matrix}{{An} = {\sum\limits_{i = 1}^{n}{{An}_{i}w_{i}}}} & (1)\end{matrix}$

[0167] wherein (An) is an analyte of interest, n is the number ofexperts, An_(i) is the analyte predicted by Expert i; and w_(i) is aparameter. The number of experts is chosen based on the quality of thefit of the model, subject to the requirement that it is desirable to usethe least number of experts possible. The number of experts ispreferably less than 100, and more preferably less than 30. In mostcases, selection of the fewest possible experts is desirable.

[0168] The individual Experts An_(i) are further defined by theexpression shown as Equation (2). $\begin{matrix}{{An}_{i} = {{\sum\limits_{j = 1}^{m}{a_{ij}P_{j}}} + z_{i}}} & (2)\end{matrix}$

[0169] wherein, An_(i) is the analyte predicted by Expert i; P_(j) isone of m parameters, m is typically less than 100; a_(ij) arecoefficients; and z_(i) is a constant.

[0170] The weighting value, w_(i), is defined by the formula shown asEquation (3). $\begin{matrix}{w_{i} = \frac{^{d_{i}}}{\left\lbrack {\sum\limits_{k = 1}^{n}^{d_{k}}} \right\rbrack}} & (3)\end{matrix}$

[0171] where e refers to the exponential function and the d_(k) (notethat the d_(i) in the numerator of Equation 3 is one of the d_(k)) are aparameter set analogous to Equation 2 that is used to determine theweights w_(i). The d_(k) are given by Equation 4. $\begin{matrix}{d_{k} = {{\sum\limits_{j = 1}^{m}{\alpha_{jk}P_{j}}} + \omega_{k}}} & (4)\end{matrix}$

[0172] where α_(jk) is a coefficient, P_(j) is one of m parameters, andwhere ω_(k) is a constant.

[0173] The Mixtures of Experts method described by the above equationsis supplied with a large data base of empirically obtained informationabout the parameters defined by the equations. By employing a linearregression function, the equations are simultaneously solved for thevalues of all coefficients and constants. In other words, the algorithmis trained to be predictive for the value of An (the analyte) given aparticular set of data. A preferred optimization method to determine thecoefficients and constants is the Expectation Maximization method(Dempster, A. P., N. M. Laird, and D. B. Rubin, J. Royal StatisticalSociety (Series B-Methodological) 39:(1), 1977). Other optimizationmethods include the Levenburg-Marquardt algorithm (Marquardt, D. W., J.Soc. Ind. Appl. Math. 11:p431-441, 1963) and the Simplex algorithm(Nelder, J. A., and Mead, R., Computer Journal 7:p308, 1965).

[0174] In the context of blood glucose monitoring with an iontophoreticsampling device, the MOE algorithm allows for the accurate prediction ofglucose concentration. In this regard, during a typical iontophoreticmeasuring cycle, iontophoretic extraction of the analyte is carried outfor a suitable amount of time, for example about 1 to 30 minutes, afterwhich time the extracted analyte is detected for a suitable amount oftime, for example about 1-30 minutes. An application of the Mixtures ofExperts algorithm to a specific set of parameters for glucose monitoringis presented in Example 1.

[0175] In the context of blood glucose monitoring with an iontophoreticsampling device, the Mixtures of Experts algorithm allows for theaccurate prediction of blood glucose concentrations.

[0176] 2.4.0 Algorithm Modifications

[0177] A further aspect of the present invention is the modification ofthe Mixtures of Experts (MOE) algorithm. The MOE can be modified in anumber of ways including, but not limited to, the followingmodifications: using different groups of selected inputs (see above);adapting the algorithm by modifying the training set; using differentalgorithms or modifications of the MOE for different ranges of analytedetection; using different statistical distributions in the Mixtures ofExperts; rejection of selected expert(s); and, switching algorithms.

[0178] 2.4.1 Adapting the Algorithm

[0179] The Mixtures of Experts (MOE) is trained using sets of data thatcontain patterns. Those patterns, represented in a training data set,typically give good performance. Accordingly, training MOE with a widevariety of patterns improves the predictive performance of MOE, forexample, using a variety of blood glucose patterns that occur indiabetics patients to obtain parameters that represent the patterns. Inthis case the selected patterns are used to develop an appropriatetraining set for MOE and then the parameters generated from thattraining set are used to test data representing a variety of patterns.In one embodiment, a “global” training set may be augmented by providinga training data set developed from an individual subject's blood glucosedata taken over several (or many) days. Such an individual pattern ispotentially useful to customize the algorithm to that subject. Theparameters generated from using a training set including such anindividual patterns is then tested in the same individual to determinewhether the expanded training data set provides better predicted values.In an alternative embodiment, a selected percentage of the globaltraining set can be used with the individual's training set (rather thanusing the entire global training set).

[0180] Further, the data comprising a training data set can bespecifically chosen to optimize performance of the MOE under specificconditions. Such optimization may include, for example, using diversedata sets or selecting the best data to represent a specific condition.For example, different training data sets based on data obtained from avariety of races can be used to train the MOE to optimize predictiveperformance for individual members of the different races represented bydifferent data sets.

[0181] Finally, MOE is typically trained with values chosen in aselected range (e.g., blood glucose values in the range of 40-400mg/dl). However, the MOE can be trained with data sets that fall outsideof the selected range.

[0182] 2.4.2 Algorithm Optimized for Different Ranges

[0183] The MOE can be optimized for predictive performance in selectedranges of data. Depending on the range different MOEs may be invoked forprediction of analyte values (see “Switching Algorithms” below).Alternatively, different algorithms can be used for prediction of valuesin selected ranges of analyte detection. For example, MOE may be usedfor prediction of glucose values in a range of 40-400 mg/dl; however, atlow and high ends of glucose values a specifically defined function canbe applied to the data in order to get preferred values. Such preferredvalues may, for example, be useful in the situation whereunder-prediction is more desirable than over-prediction (e.g., at lowblood glucose values). In this case a modification of MOE may be used oran specific algorithm may be optimized for prediction in the selectedrange using, for example, a non-linear distribution function thatemphasizes predicting low blood glucose (BG) in the range BG≦100.

[0184] 2.4.3 Employing Different Distribution Functions

[0185] When calculating the weights used in the MOE algorithm a selecteddistribution is used. One exemplary distribution is a Gaussiandistribution (Example 3) that weighs deviations relative to the squareof difference from the mean. However, other distributions can be used toimprove predictive function of the algorithm. For example, a Laplaciandistribution function was used in the calculations presented in Example4. The Laplacian distribution has longer tails than a Gaussiandistribution, and weighs deviations relative to the absolute differencefrom the mean. Other distribution functions can be used as wellincluding, but not limited to, Cauchy distribution or a specificdistribution function devised (or calculated) based on specific datasets obtained, for example, from different individuals or differentgroups of individuals (e.g., different races).

[0186] 2.4.4 Rejecting Experts

[0187] When multiple experts are used in the MOE each expert can beinspected to determine if, for example, one or more of the experts isproviding incongruous values. When such an expert is identified (e.g.,in the calculation of a particular data point) the expert may beeliminated for that calculation and the weights of the remaining expertsreadjusted appropriately. Inspection of the experts can be carried outby a separate algorithm and can, for example, be based on whether thevalue predicted by the expert falls outside of a designated range. Ifthe value falls outside of a designated range, the expert may beeliminated in that calculation. For example, Example 3 describes the useof three experts (BG₁, BG₂, and BG₃) in an MOE for prediction of bloodglucose values, wherein a weighted average is used to calculate thefinal blood glucose value. However, each of these three experts can beinspected to determine if one (or more) of them does not make sense(e.g., is providing a stochastic or out-lying value significantlydifferent from the other two experts). The expert providing theincongruous value is disregarded and the weights of the other twoexperts are readjusted accordingly.

[0188] 2.4.5 Switching Algorithms

[0189] In yet another aspect of the present invention, prediction of theconcentration of an analyte can be accomplished using specializedalgorithms, where the specialized algorithms are useful for predictionsin particular situations (e.g., particular data sets or ranges ofpredicted values) and where the algorithm used for performing thecalculations is determined based on the situation. In this case a“switch” can be used to employ one (or more) algorithm rather thananother (or more) algorithm. For example, a global MOE algorithm can bethe switch used to selected one of three different MOE algorithms. Inone embodiment such a global MOE algorithm may be used to determine ablood glucose value. The blood glucose value is determined, by thealgorithm, to fall into one of three ranges (for example, low, normal,and high). For each range there is an separate MOE algorithm thatoptimizes the prediction for values in the particular range. The globalMOE algorithm then selects the appropriate MOE algorithm based on thevalue and the selected MOE performs a new prediction of blood glucosevalues based on the original input values but optimized for the rangeinto which the value was predicted (by the global MOE) to fall. As afurther illustration, inputs to determine a blood glucose value areprovided to a global MOE which determines that the value is a low-value.The inputs are then directed to a Low-Value Optimized MOE to generate amore accurate predicted blood glucose value.

[0190] Specialized algorithms may be developed to be used in differentparts of a range of analyte signal spectrum or other input values (e.g.,high signal/low signal; high BGCal/low BGCal; high/low calratio;high/low temp; etc., for all variables used in the prediction). A globalalgorithm can be used to decide which region of the spectrum the analytesignal is in, and then the global algorithm switches the data to theappropriate specialized algorithm.

[0191] In another embodiment, an algorithm other than the MOE can beused as the switch to choose among a set of MOE algorithms, or an MOEcan be used as the switch to choose among a set of other algorithms.Further, there can be multiple levels of specialized switching (whichcan be graphically represented for instance by branched tree-diagrams).

[0192] Following here are several specific, non-limiting examples, ofthe uses of switching in the practice of the present invention whenblood glucose values are being determined.

[0193] In one embodiment, variables are identified that explicitlyrepresent signal decay, for example, a switch based on elapsed timesince calibration (early or late) or the value of Calratio at CAL (highor low). An exemplary switch of this type is represented by elapsed timesince calibration where, for example, the algorithm described in Example3 may be trained independently with inputs from an early phase of sensoruse and inputs from a late phase sensor use (e.g., the total useful lifeof a sensor element may be split into two halves—early and late). Then,depending on the time since calibration that selected input values arebeing obtained (an exemplary switch), the input values are directed toan MOE algorithm that was trained on data from the appropriate phase(i.e., either early or late). Such a switch is useful to help correctfor error based in sensor decay.

[0194] Another exemplary switch of this type is represented by the valueof Calratio at the calibration point. Calratio is described in Example4. The Calratio is a measure of sensor sensitivity. Accordingly, ifdesired the Calratio range can be divided into two halves (high and lowranges). The algorithm described in Example 3 may be trainedindependently with inputs from the high and low ranges of the Calratio.A switch is then based on the Calratio values to direct the inputs tothe MOE algorithm that is trained with the appropriate data set (i.e.,data sets corresponding to inputs from high and low Calratio ranges).

[0195] 2.5.0 Decreasing the Bias of a Data Set

[0196] In addition to the MOE algorithm described in the presentspecification, following here is a description of a method to alter dataused to generate a training data set so as to correct slope, intercept(and resultant bias) introduced by the limited range of data input. Thisinvention provides a useful correction for any asymmetric data inputthat gives a bias to resultant predictions. In this method, the valuesof a data set are used to create a second data set that mirrors thefirst, i.e., positive values become negative values (opposite signs).The two data sets are then used as the training data set. Thistransformation of the asymmetrical data set results in a forced symmetryof the data comprising the training set.

[0197] The following is a non-limiting example of this method for thecorrection of bias using blood glucose level determination. In the bloodglucose value determinations described herein there is an inherent bias(manifested by a slope of <1 and a positive intercept, e.g., FIG. 10A;in the figure, pBG is predicted blood glucose and mBG is directlymeasured blood glucose—measured, for example, using a HemoCue® meter)introduced into the prediction function. This is in part due to the factthat there are no blood glucose levels of <40 mg/dl used in the datainput training set. The data input for training the MOE algorithm, usedto predict blood glucose levels, uses, for example, the followingvariables: elapsed time since calibration, average signal, calibratedsignal, and the blood glucose at the calibration point (see, e.g.,Examples 3 and 4). The value that these inputs predict and try to matchis directly measured blood glucose. The allowed range for blood glucoseis 40-400 mg/dl. Due to this limited range of blood glucose, theresultant function predictions (i.e., via the MOE) result in an inherentbias, slope <1 and positive intercept when plotting predicted bloodglucose (y-axis variable, pBG, FIG. 10A) versus directly measured bloodglucose (x-axis variable, mBG, FIG. 10A).

[0198] The method of the present invention circumvents this problem byaugmenting the original input data set with a data set comprising thesame elapsed time since calibration, but with values of the averagesignal (in nanocoulombs) and directly measured blood glucose both of theopposite sign relative to the original, real data set. The calibratedsignal is then calculated using the opposite sign data. In this way theinput data is doubled and is now symmetric around the origin (FIG. 10B;in the figure, pBG is predicted blood glucose and mBG is directlymeasured blood glucose—measured, for example, using a HemoCue® meter).In FIG. 10B the dotted lines represent the slopes predicted from thesingle data set with which they are associated. The solid line betweenthe two dashed lines represents the corrected slope based on use of theoriginal data set and the opposite sign data set to train the algorithm.

[0199] The value of this approach when plotting predicted bloodglucose(using MOE) versus measured blood glucose can be seen byexamining the results presented in the following table. Original DataSet Original & Opposite Sign Data Set Data Set Deming Slope* 0.932 1.042Deming Intercept* 12.04 −5.63 Bias 50 mg/dl 8.64 −3.53 Bias 80 mg/dl 6.6−2.27 Bias 100 mg/dl 5.24 −1.43 Bias 150 mg/dl 1.84 0.67 Bias 200 mg/dl−1.56 2.77

[0200] As the results in this table demonstrate, the bias reducingmethod of the present invention has a slope closer to 1, an interceptcloser to zero, and the bias values are, in general, closer to zero.

[0201] Accordingly, one aspect of the present invention is a method fordecreasing the bias of a data set. The method involves generating asecond data set that has values opposite in sign of the original dataset and using this first and second data set as a combined data set totrain the algorithm (e.g., MOE).

EXAMPLES

[0202] The following examples are put forth so as to provide those ofordinary skill in the art with a complete disclosure and description ofhow to make and use the devices, methods, and formulae of the presentinvention, and are not intended to limit the scope of what the inventorregards as the invention. Efforts have been made to ensure accuracy withrespect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

Example 1

[0203] Application of the “Mixtures of Experts” to Glucose Monitoring

[0204] This example describes the use of a Mixtures of Experts (MOE)algorithm to predict blood glucose data from a series of signals.

[0205] In the present example, a GlucoWatch® monitor was used to collectdata and the following variables were chosen to generate data sets forthe MOE algorithm:

[0206] 1) elapsed time (time), elapsed time since the GlucoWatch®monitor was applied to the subject, i.e., elapsed time since thesampling system was placed in operative contact with the biologicalsystem;

[0207] 2) active signal (active), in this example, the value of theactive parameter corresponded to the nanoamp signal that was integratedover the sensing time-interval to give the active parameter innanocoulombs (nC);

[0208] 3) calibrated signal (signal), in this example was obtained bymultiplying an active by a constant, where the constant was defined asthe blood glucose level at the calibration point divided by the activevalue at the calibration point. For example, as follows:${signal} = {\frac{{BG}/{cp}}{{activite}/{cp}}({active})}$

[0209] where the slope of the line active versus blood glucose had anon-zero intercept and the offset took into account that the interceptwas not zero. In the alternative, the constant could be as follows:${signal} = {\frac{{BG}/{cp}}{\left( {{{active}/{cp}} + {offset}} \right)}\left( {{active} + {offset}} \right)}$

[0210] where the offset takes into account the intercept value.

[0211] 4) blood glucose value at the calibration point (BG/cp) wasdetermined by direct blood testing.

[0212] Other possible variables include, but are not limited to,temperature, iontophoretic voltage (which is inversely proportional toskin resistance), and skin conductivity

[0213] Large data sets were generated by collecting signals using atransdermal sampling system that was placed in operative contact withthe skin. The sampling system transdermally extracted the analyte fromthe biological system using an appropriate sampling technique (in thiscase, iontophoresis). The transdermal sampling system was maintained inoperative contact with the skin to provide a near continual orcontinuous stream of signals.

[0214] The basis of the Mixtures of Experts was to break up a non-linearprediction equation (Equation 5, below) into several Expert predictionequations, and then to have a routine to switch between the differentlinear equations. For predicting blood glucose levels, three separatelinear equations (Equations 6, 7, and 8) were used to represent bloodglucose, with the independent variables discussed above of time, active,signal, blood glucose at a calibration point (BG/cp), and a constant(ti).

[0215] The switching between equations 6, 7, and 8 was determined by theparameters w₁, w₂, and w₃ in equation 5, which was further determined bythe parameters d₁, d₂, and d₃ as given by equations 9-14, where theindividual experts had a linear form:

BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (5)

[0216] wherein (BG) was blood glucose, there are three experts (n=3);BG_(i) was the analyte predicted by Expert i; and w_(i) was a parameter,and the individual Experts BG_(i) were further defined by the expressionshown as Equations 6, 7, and 8

BG ₁ =p ₁(time)+q ₁(active)+r ₁(signal)+s ₁(BG|cp)+t₁   (6)

BG ₂ =p ₂(time)+q ₂(active)+r ₂(signal)+s ₂(BG|cp)+t₂   (7)

BG ₃ =p ₃(time)+q ₃(active)+r ₃(signal)+s ₃(BG|cp)+t₃   (8)

[0217] wherein, BG_(i) was the analyte predicted by Expert i; parametersinclude, time (elapsed time), active (active signal), signal (calibratedsignal), and BG/cp (blood glucose value at a calibration point); p_(i),q_(i), r_(i), and s_(i) were coefficients; and t_(i) was a constant; andfurther where the weighting value, w_(i), was defined by the formulasshown as Equations 9, 10, and 11 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (9) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (10) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (11)\end{matrix}$

[0218] where e referred to the exponential function and d_(i) was aparameter set (analogous to Equations 6, 7, and 8) that were used todetermine the weights w_(i), given by Equations 9, 10, and 11, and

d ₁=τ₁(time)+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (12)

d ₂=τ₂(time)+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (13)

d ₃=τ₃(time)+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (14)

[0219] where τ_(i), β_(i), γ_(i) and δ_(i) were coefficients, and where∈_(i) is a constant.

[0220] To calculate the above parameters an optimization method wasapplied to the algorithm (Equations 5-14) and the large data set. Theoptimization method used was the Expectation Maximization method(Dempster, A. P., N. M. Laird, and D. B. Rubin, J. Royal StatisticalSociety (Series B-Methodological) 39:(1), 1977), but other methods maybe used as well.

[0221] The parameters in these equations were determined such that theposterior probability of the actual glucose was maximized.

Example 2

[0222] Prediction of Measurement Values I

[0223] Iontophoretic extraction of glucose was carried out using aGlucoWatch® monitor which employs (i) a low-level iontophoretic currentto extract glucose through patient's skin, and (ii) an electrochemicalbiosensor to detect the extracted glucose. Iontophoresis was carried outfor 3 minute intervals and electrochemical detection was carried out for7 minute intervals to result in 10 minute measurement cycles—thusgenerating collections of data (data sets) as described in Example 1.

[0224] The data that were used for this analysis were obtained bydiabetic subjects each wearing a GlucoWatch® monitor over a 14 hourperiod. The MOE inputs consisted of the following parameters (describedin Example 1): time, active, signal, blood glucose at a calibrationpoint (BG/cp). These training data were used to determine the unknownparameters in the MOE using the Expectation Maximization Method. Theoutput of the MOE algorithm was the measured value of blood glucose.Using a three hour time point for calibrating the GlucoWatch® monitor,the mean percentage error (MPE) between the actual blood glucose and thecalculated (MOE predicted) blood glucose was 13%.

[0225] In a diabetic study consisting of 61 patients, the diabeticsubjects' blood glucose ranged from 23-389 mg/dl. A protocol wasfollowed whereby a subject (who had fasted since the previous midnight)came to a test site where the GlucoWatch® monitor was applied to thesubject, started, and calibrated. Over the next 14 hours, the subjecthad normal meals and a finger prick blood sample was taken every 20minutes for glucose determination (“actual glucose”). Blood glucoselevels were measured using the HemoCue® meter (HemoCue AB, Sweden),which has an accuracy of ±10%.

[0226] A plot of the glucose levels predicted by the Mixtures of Expertsalgorithm (based on the data described above) versus the actual bloodglucose levels is presented in FIG. 6 (a Correlation Plot). Analysis ofthe data shown in FIG. 6 showed a slope of 0.88, an intercept of 14, anda correlation coefficient of R=0.93. There were N=1,348 pointscomprising the Correlation Plot.

[0227] These statistical results, along with the MPE=0.13 (discussedabove), show the excellent predictive capabilities of the GlucoWatch®monitor and the Mixtures of Experts algorithm.

Example 3

[0228] Another Application of the “Mixtures of Experts” to GlucoseMonitoring

[0229] This example describes the use of a Mixtures of Experts (MOE)algorithm to predict blood glucose data from a series of signals.

[0230] In the present example, a GlucoWatch® monitor was used to collectdata and the following variables were chosen to generate data sets forthe MOE algorithm:

[0231] 1) time since calibration (time_(c)), the elapsed time since thecalibration step was carried out for the GlucoWatch® monitor (in hours);

[0232] 2) active signal (active), in this example, the value of theactive parameter corresponded to the averaged signal from two activereservoirs, where each reservoir provided a nanoamp signal that wasintegrated over the sensing time-interval, the two values were thenadded and averaged to give the active parameter in nanocoulombs (nC);

[0233] 3) calibrated signal (signal), in this example was obtained asfollows:${signal} = {\frac{{BG}/{cp}}{\left( {{{active}/{cp}} + {offset}} \right)}\left( {{active} + {offset}} \right)}$

[0234] where the offset takes into account the intercept value.

[0235] 4) blood glucose value at the calibration point (BG/cp), inmg/dl, was determined by direct blood testing.

[0236] Other possible variables include, but are not limited to,temperature, iontophoretic voltage (which is inversely proportional toskin resistance), and skin conductivity.

[0237] Large data sets were generated by collecting signals using atransdermal sampling system that was placed in operative contact withthe skin. The sampling system transdermally extracted the analyte fromthe biological system using an appropriate sampling technique (in thiscase, iontophoresis). The transdermal sampling system was maintained inoperative contact with the skin to provide a near continual orcontinuous stream of signals.

[0238] The basis of the Mixtures of Experts was to break up a non-linearprediction equation (Equation 15, below) into several Expert predictionequations, and then to have a routine to switch between the differentlinear equations. For predicting blood glucose levels, three separatelinear equations (Equations 16, 17, and 18) were used to represent bloodglucose, with the independent variables discussed above of time, active,signal, blood glucose at a calibration point (BG/cp), and a constant(t_(i)).

[0239] The switching between Equations 16, 17, and 18 was determined bythe parameters w₁, w₂, and w₃ in equation 5, which was furtherdetermined by the parameters d₁, d₂, and d₃ as given by equations 9-14,where the individual experts had a linear form:

BG =w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (15)

[0240] wherein (BG) was blood glucose, there are three experts (n=3);BG_(i) was the analyte predicted by Expert i; and w_(i) was a parameter,and the individual Experts BG_(i) were further defined by the expressionshown as Equations 16, 17, and 18

BG ₁ =p ₁(time_(c))+q ₁(active)+r ₁(signal)+s ₁(BG|cp)+t ₁   (16)

BG ₂ =p ₂(time_(c))+q ₂(active)+r ₂(signal)+s ₂(BG|cp)+t ₂   (17)

BG ₃ =p ₃(time_(c))+q ₃(active)+r ₃(signal)+s ₃(BG|cp)+t ₃   (18)

[0241] wherein, BG_(i) was the analyte predicted by Expert i; parametersinclude, time_(c) (elapsed time since calibration), active (activesignal), signal (calibrated signal), and BG/cp (blood glucose value at acalibration point); p_(i), q_(i), r_(i), and s_(i) were coefficients;and t_(i) was a constant; and further where the weighting value, w_(i),was defined by the formulas shown as Equations 19, 20, and 21$\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (19) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (20) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (21)\end{matrix}$

[0242] where e referred to the exponential function and d_(i) was aparameter set (analogous to Equations 16, 17, and 18) that were used todetermine the weights w_(i), given by Equations 19, 20, and 21, and

d ₁=τ₁(time_(c))+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (22)

d ₂=τ₂(time_(c))+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (23)

d ₃=τ₃(time_(c))+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (24)

[0243] where τ_(i), β_(i), γ_(i) and δ_(i) were coefficients, and where∈_(i) is a constant.

[0244] To calculate the above parameters an optimization method wasapplied to the algorithm (Equations 15-24) and the large data set. Theoptimization method used was the Expectation Maximization method(Dempster, A. P., N. M. Laird, and D. B. Rubin, J. Royal StatisticalSociety (Series B-Methodological) 39:(1), 1977), but other methods maybe used as well.

[0245] The parameters in these equations were determined such that theposterior probability of the actual glucose was maximized.

Example 4

[0246] Prediction of Measurement Values II

[0247] A. Calibration Ratio Check

[0248] In order to insure an efficacious calibration of the samplingsystem, the value of the following ratio was found to fall in a selectedrange:${CalRatio} = \frac{{BG}/{cp}}{\left( {{{active}/{cp}} + {offset}} \right)}$

[0249] where the offset takes into account the intercept value. Therange is established using standard error minimization routines toevaluate a large population of calibration points, and thereby determinethe CalRatio values which result in accurate blood glucose predictions.In one embodiment, the preferred CalRatio range of values was between0.00039 and 0.01. In the CalRatio, BG/cp was the blood glucoseconcentration at the calibration point (or calibration time), active wasthe input prediction at the calibration point, and offset was a constantoffset. The offset value was established empirically using standarderror minimization routines to evaluate a number of potential offsetvalues for a large data set, and thereby select the one that results inthe most accurate prediction of blood glucose.

[0250] The CalRatio check provides a screen for valid or efficaciouscalibration readings. If the CalRatio falls outside of the range ofselected values, then the calibration was rejected and the calibrationwas re-done. Low values of this ratio indicated low sensitivity ofglucose detection.

[0251] B. Prediction of Values

[0252] GlucoWatch® monitors (Cygnus, Inc., Redwood City, Calif., USA)were applied to the lower forearm of human subjects with diabetes(requiring insulin injection). Iontophoretic extraction of glucose wascarried out using the GlucoWatch® monitor which employs (i) a low-leveliontophoretic current to extract glucose through patient's skin, and(ii) an electrochemical biosensor to detect the extracted glucose.

[0253] The subjects were 18 years of age, or older, and consisted ofboth males and females from a broad ethnic cross-section. Iontophoresiswas carried out for 3 minute intervals and electrochemical detection wascarried out for 7 minute intervals to result in 10 minute measurementcycles—thus generating collections of data (data sets) as described inExample 3. As described in Example 3, the active measurement was theaveraged signal from two active reservoirs, for example, a firstelectrode acts as the cathode during the first 10 minute cycle (3minutes of iontophoresis, followed by 7 minutes of sensing) and a secondelectrode acts as the cathode during the second 10 minute cycle. Thecombined cycle requires 20 minutes, and the combined cathode sensor datais used as a measure of the glucose extracted (an averaged “activesignal”, see Example 3). This 20 minute cycle is repeated throughoutoperation of the GlucoWatch® monitor.

[0254] In addition, subjects obtained two capillary blood samples perhour, and the glucose concentration was determined using a HemoCue®clinical analyzer (HemoCue AB, Sweden). The blood glucose measurementobtained at three hours was used as a single point calibration, whichwas used to calculate the extracted blood glucose for all subsequentGlucoWatch® monitor measurements.

[0255] The data that were used for this analysis were obtained bydiabetic subjects each wearing two GlucoWatch® monitors over a 14 hourperiod. The MOE inputs consisted of the following parameters (describedin Example 3): time_(c), active, signal, blood glucose at a calibrationpoint (BG/cp). For the calibrated signal:${signal} = {{{BG}/{cp}}\frac{\left( {{active} + {offset}} \right)}{\left( {{{active}/{cp}} + {offset}} \right)}}$

[0256] where (i) active/cp was the input prediction at the calibrationpoint, and (ii) the offset and takes into account the fact that whenpredicted blood glucose is plotted vs. active, there is a non-zeroy-intercept. The optimized value of the offset that was used was aconstant value of 1000 nC. The signal that is used in the Mixtures ofExperts algorithm is temperature compensated by applying an Arrheniustype correction to the raw signal data to account for skin temperaturefluctuations.

[0257] Finally, in order to eliminate potential outlier points, variousscreens were applied to the raw and integrated sensor signals. Thepurpose of these screens were to determine whether certainenvironmental, physiological or technical conditions existed during ameasurement cycle that could result in an erroneous reading. The screensthat were used measured the averaged signal (active), iontophoreticvoltage, temperature, and skin surface conductance. If any of thesemeasurements deviated sufficiently from predefined behavior during ameasurement, then the entire measurement was excluded. For example, ifthe skin surface conductance exceeded a set threshold, which indicatedexcessive sweating (sweat contains glucose), then this potentiallyerroneous measurement was excluded. These screens enable very noisy datato be removed, while enabling the vast majority of points (>87%) to beaccepted.

[0258] The Mixtures of Experts was further customized in the followingway. When the weights were updated using equations 19-24 (Example 3), aLaplacian distribution function was used. The Laplacian distribution haslonger tails than a Gaussian distribution, and weighs deviationsrelative to the absolute difference from the mean, whereas a Gaussiandistribution weighs deviations relative to the square of difference fromthe mean (P. McCullagh and J. A. Nelder, Generalized Linear Models,Chapman and Hall, 1989; and W. H. Press, S. A. Teukolsky, W. T.Vetterling and B. P. Flannery, Numerical Recipes in C. CambridgeUniversity Press, Cambridge, 1992). In addition, the individual bloodglucose values were weighted by the inverse of the value of the bloodglucose at the calibration point. Both of these modifications result inincreased accuracy of predictions, especially at low blood glucoselevels.

[0259] The training data were used to determine the unknown parametersin the Mixtures of Experts using the Expectation Maximization Method.The Mixtures of Experts algorithm was trained until convergence of theweights was achieved. The output of the MOE algorithm was the measuredvalue of blood glucose. Using a three hour time point for calibratingthe GlucoWatch® monitor, the mean percentage error (MPE) between theactual blood glucose and the calculated (MOE predicted) blood glucosewas 14.4%.

[0260] In a diabetic study consisting of 91 GlucoWatch® monitors, thediabetic subjects' blood glucose ranged from 40-360 mg/dl. A protocolwas followed whereby a subject (who had fasted since the previousmidnight) came to a test site where two GlucoWatch® monitors wereapplied to the subject, started, and calibrated. Over the next 14 hours,the subject had normal meals and a finger prick blood sample was takenevery 20 minutes for glucose determination (“actual glucose”). Bloodglucose levels were measured using the HemoCue® meter (HemoCue AB,Sweden), which has an accuracy of ±10%.

[0261] A plot of the glucose levels predicted by the Mixtures of Expertsalgorithm (based on the data described above) versus the actual bloodglucose levels is presented in FIG. 7 (a Correlation Plot). Also shownin FIG. 7 is the orthogonal least squares line (A. Madansky, The Fittingof Straight Lines When both Variables are Subject to Error, J. AmericanStatistical Association 54:173-206, 1959; D. York, Least-Squares Fittingof a Straight Line, Canadian Journal of Physics 44:1079-1986, 1966; W.A. Fuller, Measurement Error Models, Wiley, New York, 1987; and W. H.Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, NumericalRecipes in C. Cambridge University Press, Cambridge, 1992) with an errorvariance ratio (defined as the error in the dependent variable dividedby the error of the independent variable) of 2.05. This variance errorratio corrects the linear regression line (which assumes zero error inthe independent variable) for the true error in both independent anddependent variables.

[0262] The variance ratio was determined as follows. Each subject wasrequired to wear two GlucoWatch® monitors. Then, at each time point, thedifference between the two watches was determined, squared and dividedby 2. The resulting values were averaged over the total number of timepoints used. Fifty pairs of watches, each with 42 time points, were usedfor this calculation. The error variance for the HemoCue® was obtainedfrom clinical data published in the literature. The GlucoWatch® monitorerror variance was calculated to be 150 (standard deviation=12 mg/dl)and the HemoCue® error variance was calculated to be 73 (standarddeviation=8.5 mg/dl), giving the error ratio of 2.05.

[0263] Analysis of the data shown in FIG. 7 showed a slope of 1.04, anintercept of approximately −10.7 mg/dl, and a correlation coefficient ofR=0.89.

[0264] It is also instructive to examine graphs of the measured andpredicted blood glucose levels vs. time. One such graph is shown in FIG.8 (in the legend of FIG. 8: solid diamonds-are measurements obtainedusing the GlucoWatch® monitor; open circles are blood glucoseconcentrations as determined using HemoCue®; and the “star” symbolrepresents blood glucose concentration at the calibration point). FIG. 8indicates the excellent capabilities of the GlucoWatch® monitor and theMixtures of Experts algorithm in calibrating the device.

[0265] These statistical results, along with the MPE=14.4% (discussedabove), show the excellent predictive capabilities of the GlucoWatch®monitor and the Mixtures of Experts algorithm.

[0266] Although preferred embodiments of the subject invention have beendescribed in some detail, it is understood that obvious variations canbe made without departing from the spirit and the scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A method for continually or continuouslymeasuring an analyte present in a biological system, said methodcomprising: (a) transdermally extracting the analyte from the biologicalsystem using a sampling system that is in operative contact with a skinor mucosal surface of said biological system; (b) obtaining a raw signalfrom the extracted analyte, wherein said raw signal is specificallyrelated to the analyte; (c) performing a calibration step whichcorrelates the raw signal obtained in step (b) with a measurement valueindicative of the concentration of analyte present in the biologicalsystem at the time of extraction; (d) repeating steps (a)-(b) to obtaina series of measurement values at selected time intervals, wherein thesampling system is maintained in operative contact with the skin ormucosal surface of said biological system to provide for a continual orcontinuous analyte measurement; and (e) predicting a measurement valuebased on the series of measurement values using the Mixtures of Expertsalgorithm, where the individual experts have a linear form$\begin{matrix}{{An} = {\sum\limits_{i = 1}^{n}{{An}_{i}w_{i}}}} & (1)\end{matrix}$

wherein (An) is an analyte of interest, n is the number of experts,An_(i) is the analyte predicted by Expert i; and w_(i) is a parameter,and the individual experts An_(i) are further defined by the expressionshown as Equation (2) $\begin{matrix}{{An}_{i} = {{\sum\limits_{j = 1}^{m}{a_{ij}P_{j}}} + z_{i}}} & (2)\end{matrix}$

wherein, An_(i) is the analyte predicted by Expert i; P_(j) is one of mparameters, m is typically less than 100; a_(ij) are coefficients; andz_(i) is a constant; and further where the weighting value, w_(i), isdefined by the formula shown as Equation (3) $\begin{matrix}{w_{i} = \frac{^{d_{i}}}{\left\lbrack {\sum\limits_{k = 1}^{n}^{d_{k}}} \right\rbrack}} & (3)\end{matrix}$

where e refers to the exponential function and the d_(k) (note that thed_(i) in the numerator of Equation 3 is one of the d_(k)) are aparameter set analogous to Equation 2 that is used to determine theweights w_(i). The d_(k) are given by Equation 4 $\begin{matrix}{d_{k} = {{\sum\limits_{j = 1}^{m}{\alpha_{jk}P_{j}}} + \omega_{k}}} & (4)\end{matrix}$

where α_(jk) is a coefficient, P_(j) is one of m parameters, and whereω_(k) is a constant.
 2. The method of claim 1, wherein the analyte isextracted from the biological system in step (a) into a collectionreservoir to obtain a concentration of the analyte in said reservoir. 3.The method of claim 2, wherein the collection reservoir is in contactwith the skin or mucosal surface of the biological system and theanalyte is extracted using an iontophoretic current applied to said skinor mucosal surface.
 4. The method of claim 3, wherein the collectionreservoir contains an enzyme that reacts with the extracted analyte toproduce an electrochemically detectable signal.
 5. The method of claim4, wherein the analyte is glucose.
 6. The method of claim 5, wherein theenzyme is glucose oxidase.
 7. The method of claim 1, wherein theprediction of step (e) is carried out using said series of two or moremeasurement values in an algorithm represented by the Mixtures ofExperts algorithm, where the individual experts have a linear form BG=w₁ BG ₁ +w ₂ BG ₂ +w ₃   (5) wherein (BG) is blood glucose, there arethree experts (n=3) and BG_(i) is the analyte predicted by Expert i;w_(i) is a parameter, and the individual Experts BG_(i) are furtherdefined by the expression shown as Equations 6, 7, and 8 BG ₁ =p₁(time)+q ₁(active)+r ₁(signal)+s ₁(BG|cp)+t ₁   (6) BG ₂ =p ₂(time)+q₂(active)+r ₂(signal)+s ₂(BG|cp)+t ₂   (7) BG ₃ =p ₃(time)+q ₃(active)+r₃(signal)+s ₃(BG|cp)+t ₃   (8) wherein, BG_(i) is the analyte predictedby Expert i; parameters include, time (elapsed time since the samplingsystem was placed in operative contact with said biological system),active (active signal), signal (calibrated signal), and BG/cp (bloodglucose value at a calibration point); p_(i), q_(i), r_(i), and s_(i)are coefficients; and t_(i) is a constant; and further where theweighting value, w_(i), is defined by the formulas shown as Equations 9,10, and 11 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (9) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (10) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (11)\end{matrix}$

where e refers to the exponential function and d_(i) is a parameter set(analogous to Equations 6, 7, and 8) that are used to determine theweights w_(i), given by Equations 9, 10, and 11, and d₁=τ₁(time)+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (12) d₂=τ₂(time)+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (13) d₃=τ₃(time)+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (14) where τ_(i), β_(i),γ_(i) and δ_(i) are coefficients, and where ∈_(i) is a constant.
 8. Themethod of claim 1, wherein the prediction of step (e) is carried outusing said series of two or more measurement values in an algorithmrepresented by the Mixtures of Experts algorithm, where the individualexperts have a linear form BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (15)wherein (BG) is blood glucose, there are three experts (n=3) and BG_(i)is the analyte predicted by Expert i; w_(i) is a parameter, and theindividual Experts BG_(i) are further defined by the expression shown asEquations 16, 17, and 18 BG ₁ =p ₁(time_(c))+q ₁(active)+r ₁(signal)+s₁(BG|cp)+t ₁   (16) BG ₂ =p ₂(time_(c))+q ₂(active)+r ₂(signal)+s₂(BG|cp)+t ₂   (17) BG ₃ =p ₃(time_(c))+q ₃(active)+r ₃(signal)+s₃(BG|cp)+t ₃   (18) wherein, BG_(i) is the analyte predicted by Experti; parameters include, time_(c) (elapsed time from a calibration of saidsampling system), active (active signal), signal (calibrated signal),and BG/cp (blood glucose value at a calibration point); p_(i), q_(i),r_(i), and s_(i) are coefficients; and t_(i) is a constant; and furtherwhere the weighting value, w_(i), is defined by the formulas shown asEquations 19, 20, and 21 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (19) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (20) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (21)\end{matrix}$

where e refers to the exponential function and d_(i) is a parameter set(analogous to Equations 6, 7, and 8) that are used to determine theweights w_(i), given by Equations 19, 20, and 21, and d ₁ =τ₁(time_(c))+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (22) d ₂ =τ₂(time_(c))+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (22) d ₃ =τ₃(time_(c))+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (22) where τ_(i),β_(i), γ_(i) and δ_(i) are coefficients, and where ∈_(i) is a constant.9. The method of either of claim 7 or claim 8, which includes furtherparameters for measurement values selected from the group consisting oftemperature, ionophoretic voltage, and skin conductivity.
 10. A methodfor measuring blood glucose in a subject, said method comprising: (a)obtaining a raw signal from a sensing apparatus, wherein said raw signalis specifically related to the glucose detected by the sensingapparatus; (b) performing a calibration step which correlates the rawsignal obtained in step (a) with a measurement value indicative of thesubject's blood glucose concentration; (c) repeating step (a) to obtaina series of measurement values at selected time intervals; and (d)predicting a measurement value using the Mixtures of Experts algorithm,where the individual experts have a linear form: $\begin{matrix}{{An} = {\sum\limits_{i = 1}^{n}{{An}_{i}w_{i}}}} & (1)\end{matrix}$

wherein (An) is blood glucose value, n is the number of experts, An_(i)is the blood glucose value predicted by Expert i; and w_(i) is aparameter, and the individual experts An_(i) are further defined by theexpression shown as Equation (2) $\begin{matrix}{{An}_{i} = {{\sum\limits_{j = 1}^{m}{a_{ij}P_{j}}} + z_{i}}} & (2)\end{matrix}$

wherein, An_(i) is the blood glucose value predicted by Expert i; P_(j)is one of m parameters, m is typically less than 100; a_(ij) arecoefficients; and z_(i) is a constant; and further where the weightingvalue, w_(i), is defined by the formula shown as Equation (3),$\begin{matrix}{w_{i} = \frac{^{d_{i}}}{\left\lbrack {\sum\limits_{k = 1}^{n}\quad ^{d_{k}}} \right\rbrack}} & (3)\end{matrix}$

where e refers to the exponential function and the d_(k) (note that thed_(i) in the numerator of Equation 3 is one of the d_(k)) are aparameter set analogous to Equation 2 that is used to determine theweights w_(i). The d_(k) are given by Equation 4 $\begin{matrix}{d_{k} = {{\sum\limits_{j = 1}^{m}\quad {\alpha_{jk}P_{j}}} + \omega_{k}}} & (4)\end{matrix}$

where α_(jk) is a coefficient, P_(j) is one of m parameters, and whereω_(k) is a constant.
 11. The method of claim 10, where in said Mixturesof Experts algorithm, the individual experts have a linear form BG=w ₁BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (5) wherein (BG) is blood glucose, there arethree experts (n=3) and BG_(i) is the analyte predicted by Expert i;w_(i) is a parameter, and the individual Experts BG_(i) are furtherdefined by the expression shown as Equations 6, 7, and 8 BG ₁ =p₁(time)+q ₁(active)+r₁(signal)+s ₁(BG|cp)+t ₁   (6) BG ₂ =p ₂(time)+q₂(active)+r₂(signal)+s ₂(BG|cp)+t ₂   (7) BG ₃ =p ₃(time)+q₃(active)+r₃(signal)+s ₃(BG|cp)+t ₃   (8) wherein, BG_(i) is the analytepredicted by Expert i; parameters include, time (elapsed time since thesampling system was placed in operative contact with said biologicalsystem), active (active signal), signal (calibrated signal), and BG/cp(blood glucose value at a calibration point); p_(i), q_(i), r_(i), ands_(i) are coefficients; and t_(i) is a constant; and further where theweighting value, w_(i), is defined by the formulas shown as Equations 9,10, and 11 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (9) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (10) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (11)\end{matrix}$

where e refers to the exponential function and d_(i) is a parameter set(analogous to Equations 6, 7, and 8) that are used to determine theweights w_(i), given by Equations 9, 10, and 11, and d₁=τ₁(time)+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (12) d₂=τ₂(time)+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (13) d₃=τ₃(time)+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (14) where τ_(i), β_(i),γ_(i) and δ_(i) are coefficients, and where ∈_(i) is a constant.
 12. Themethod of claim 10, where in said Mixtures of Experts algorithm, theindividual experts have a linear form BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃  (15) wherein (BG) is blood glucose, there are three experts (n=3) andBG_(i) is the analyte predicted by Expert i; w_(i) is a parameter, andthe individual Experts BG_(i) are further defined by the expressionshown as Equations 16, 17, and 18 BG ₁ =p ₁(time_(c))+q ₁(active)+r₁(signal)+s ₁(BG|cp)+t ₁   (16) BG ₂ =p ₂(time_(c))+q ₂(active)+r₂(signal)+s ₂(BG|cp)+t ₂   (17) BG ₃ =p ₃(time_(c))+q ₃(active)+r₃(signal)+s ₃(BG|cp)+t ₃   (18) wherein, BG_(i) is the analyte predictedby Expert i; parameters include, time_(c) (elapsed time from acalibration of said sampling system), active (active signal), signal(calibrated signal), and BG/cp (blood glucose value at a calibrationpoint) ; p_(i), q_(i), r_(i), and s_(i) are coefficients; and t_(i) is aconstant; and further where the weighting value, w_(i), is defined bythe formulas shown as Equations 19, 20, and 21 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (19) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (20) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (21)\end{matrix}$

where e refers to the exponential function and d_(i) is a parameter set(analogous to Equations 6, 7, and 8) that are used to determine theweights w_(i), given by Equations 19, 20, and 21, and d₁=τ₁(time_(c))+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (22) d₂=τ₂(time_(c))+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (23) d₃=τ₃(time_(c))+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (24) where τ_(i),β_(i), γ_(i) and δ_(i) are coefficients, and where ∈_(i) is a constant.13. The method of either claim 11 or claim 12, wherein the sensingapparatus is a near-IR spectrometer.
 14. The method of either claim 11or claim 12, wherein the sensing means comprises a biosensor having anelectrochemical sensing element.
 15. A monitoring system for continuallyor continuously measuring an analyte present in a biological system,said system comprising, in operative combination: (a) sampling means forcontinually or continuously extracting the analyte from the biologicalsystem, wherein said sampling means is adapted for extracting theanalyte across a skin or mucosal surface of said biological system; (b)sensing means in operative contact with the analyte extracted by thesampling means, wherein said sensing means obtains a raw signal from theextracted analyte and said raw signal is specifically related to theanalyte; and (c) microprocessor means in operative communication withthe sampling means and the sensing means, wherein said microprocessormeans (i) is used to control the sampling means and the sensing means toobtain a series of raw signals at selected time intervals during acontinual or continuous measurement period, (ii) correlate the rawsignals with measurement values indicative of the concentration ofanalyte present in the biological system, and (iii) predict ameasurement value using the Mixtures of Experts algorithm, where theindividual experts have a linear form $\begin{matrix}{{An} = {\sum\limits_{i = 1}^{n}\quad {{An}_{i}w_{i}}}} & (1)\end{matrix}$

wherein (An) is an analyte of interest, n is the number of experts,An_(i) is the analyte predicted by Expert i; and w_(i) is a parameter,and the individual experts An_(i) are further defined by the expressionshown as Equation (2) $\begin{matrix}{{An}_{i} = {{\sum\limits_{j = 1}^{m}\quad {a_{ij}P_{j}}} + z_{i}}} & (2)\end{matrix}$

wherein, An_(i) is the analyte predicted by Expert i; P_(j) is one of mparameters, m is typically less than 100; a_(ij) are coefficients; andz_(i) is a constant; and further where the weighting value, w_(i), isdefined by the formula shown as Equation (3) $\begin{matrix}{w_{i} = \frac{^{d_{i}}}{\left\lbrack {\sum\limits_{k = 1}^{n}\quad ^{d_{k}}} \right\rbrack}} & (3)\end{matrix}$

where e refers to the exponential function and the d_(k) (note that thed_(i) in the numerator of Equation 3 is one of the d_(k)) are aparameter set analogous to Equation 2 that is used to determine theweights w_(i). The d_(k) are given by Equation 4 $\begin{matrix}{d_{k} = {{\sum\limits_{j = 1}^{m}\quad {\alpha_{jk}P_{j}}} + \omega_{k}}} & (4)\end{matrix}$

where α_(jk) is a coefficient, P_(j) is one of m parameters, and whereω_(k) is a constant.
 16. The monitoring system of claim 15, wherein thesampling means includes one or more collection reservoirs for containingthe extracted analyte.
 17. The monitoring system of claim 16, whereinthe sampling means uses an iontophoretic current to extract the analytefrom the biological system.
 18. The monitoring system of claim 17,wherein the collection reservoir contains an enzyme that reacts with theextracted analyte to produce an electrochemically detectable signal. 19.The monitoring system of claim 18, wherein the analyte is glucose andthe enzyme is glucose oxidase.
 20. A monitoring system for measuringblood glucose in a subject, said system comprising, in operativecombination: (a) sensing means in operative contact with the subject orwith a glucose-containing sample extracted from the subject, whereinsaid sensing means obtains a raw signal specifically related to bloodglucose in the subject; and (b) microprocessor means in operativecommunication with the sensing means, wherein said microprocessor means(i) is used to control the sensing means to obtain a series of rawsignals at selected time intervals, (ii) correlates the raw signals withmeasurement values indicative of the concentration of blood glucosepresent in the subject, and (iii) predicts a measurement value at afurther time interval using the Mixtures of Experts algorithm, where theindividual experts have a linear form BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃  (5) wherein (BG) is blood glucose, there are three experts (n=3) andBG_(i) is the analyte predicted by Expert i; w_(i) is a parameter, andthe individual Experts BG_(i) are further defined by the expressionshown as Equations 6, 7, and 8 BG ₁ =p ₁(time)+q ₁(active)+r ₁(signal)+s₁(BG|cp)+t ₁   (6) BG ₂ =p ₂(time)+q ₂(active)+r ₂(signal)+s ₂(BG|cp)+t₂   (7) BG ₃ =p ₃(time)+q ₃(active)+r ₃(signal)+s ₃(BG|cp)+t ₃   (8)wherein, BG_(i) is the analyte predicted by Expert i; parametersinclude, time (elapsed time since the sampling system was placed inoperative contact with said biological system), active (active signal),signal (calibrated signal), and BG/cp (blood glucose value at acalibration point); p_(i), q_(i), r_(i), and s_(i) are coefficients; andt_(i) is a constant; and further where the weighting value, w_(i), isdefined by the formulas shown as Equations 9, 10, and 11 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (9) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (10) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (11)\end{matrix}$

where e refers to the exponential function and d_(i) is a parameter set(analogous to Equations 6, 7, and 8) that are used to determine theweights w_(i), given by Equations 9, 10, and 11, and d₁=τ₁(time)+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (12) d₂=τ₂(time)+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (13) d₃=τ₃(time)+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (14) where τ_(i), β_(i),γ_(i) and δ_(i) are coefficients, and where ∈_(i) is a constant.
 21. Amonitoring system for measuring blood glucose in a subject, said systemcomprising, in operative combination: (a) sensing means in operativecontact with the subject or with a glucose-containing sample extractedfrom the subject, wherein said sensing means obtains a raw signalspecifically related to blood glucose in the subject; and (b)microprocessor means in operative communication with the sensing means,wherein said microprocessor means (i) is used to control the sensingmeans to obtain a series of raw signals at selected time intervals, (ii)correlates the raw signals with measurement values indicative of theconcentration of blood glucose present in the subject, and (iii)predicts a measurement value at a further time interval using theMixtures of Experts algorithm, where the individual experts have alinear form BG=w ₁ BG ₁ +w ₂ BG ₂ +w ₃ BG ₃   (15) wherein (BG) is bloodglucose, there are three experts (n=3) and BG_(i) is the analytepredicted by Expert i; w_(i) is a parameter, and the individual ExpertsBG_(i) are further defined by the expression shown as Equations 16, 17,and 18 BG₁ =p ₁(time_(c))+q ₁(active)+r ₁(signal)+s ₁(BG|cp)+t ₁   (16)BG₂ =p ₂(time_(c))+q ₂(active)+r ₂(signal)+s ₂(BG|cp)+t ₂   (17) BG₃ =p₃(time_(c))+q ₃(active)+r ₃(signal)+s ₃(BG|cp)+t ₃   (18) wherein,BG_(i) is the analyte predicted by Expert i; parameters include,time_(c) (elapsed time from a calibration of said sampling system),active (active signal), signal (calibrated signal), and BG/cp (bloodglucose value at a calibration point); p_(i), q_(i), r_(i), and s_(i)are coefficients; and t_(i) is a constant; and further where theweighting value, w_(i), is defined by the formulas shown as Equations19, 20, and 21 $\begin{matrix}{w_{1} = \frac{^{d_{1}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (19) \\{w_{2} = \frac{^{d_{2}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (20) \\{w_{3} = \frac{^{d_{3}}}{^{d_{1}} + ^{d_{2}} + ^{d_{3}}}} & (21)\end{matrix}$

where e refers to the exponential function and d_(i) is a parameter set(analogous to Equations 6, 7, and 8) that are used to determine theweights w_(i), given by Equations 19, 20, and 21, and d₁τ₁(time_(c))+β₁(active)+γ₁(signal)+δ₁(BG|cp)+∈₁   (22) d₂τ₂(time_(c))+β₂(active)+γ₂(signal)+δ₂(BG|cp)+∈₂   (23) d₃τ₃(time_(c))+β₃(active)+γ₃(signal)+δ₃(BG|cp)+∈₃   (24) where τ_(i),β_(i), γ_(i) and δ_(i) are coefficients, and where ∈_(i) is a constant.22. The monitoring system of either claim 20 or claim 21, which includesfurther parameters for raw signals selected from the group consisting oftemperature, ionophoretic voltage, and skin conductivity.
 23. Themonitoring system of either claim 20 or claim 21, wherein the sensingmeans comprises a biosensor having an electrochemical sensing element.24. The monitoring system of either claim 20 or claim 21, wherein thesensing means comprises a near-IR spectrometer.